san %>% 
  #filter(!is.na(zipcode)) %>% 
  ggplot(aes(x = zipcode, fill = duration_min_out)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2, color = "black") +
  scale_fill_brewer(type = "div", palette = 'Spectral') +
  #facet_grid(~ zipcode) +
  ggtitle("Duration between 0.7 min and 97.3 min = NORMAL (95% of responses)") +
  xlab("Zip code") + 
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, size = 9), 
        panel.background = element_rect(fill = "gray95"))

san %>% 
  #filter(!is.na(zipcode)) %>% 
  ggplot(aes(x = zipcode, fill = duration_min_out2)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2, color = "black") +
  scale_fill_brewer(type = "div", palette = 'Spectral') +
  #facet_grid(~ zipcode) +
  ggtitle("Duration between 1.36 min and 71.2 min = NORMAL (90% of reponses)") +
  xlab("Zip code") + 
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, size = 9), 
        panel.background = element_rect(fill = "gray95"))

san %>% 
  #filter(!is.na(zipcode)) %>% 
  ggplot(aes(x = zipcode, fill = nas_out)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2, color = "black") +
  scale_fill_brewer(type = "div", palette = 'Spectral') +
  #facet_grid(~ zipcode) +
  ggtitle("Number of NAs between 5 and 58 = NORMAL") +
  xlab("Zip code") + 
  theme_classic() +
  theme(axis.text.x = element_text(angle = 90, size = 9), 
        panel.background = element_rect(fill = "gray95"))

plot_ly(data = san, x = ~nas_pct, y = ~duration, color = ~duration_min_out2) %>% 
  layout(title = "Duration (min) by NAs by response (%)", 
         yaxis = list(title = "Duration (min)"),
         xaxis = list(title = "Percentage of NA values"))
san %>% 
  filter(!is.na(zipcode)) %>% 
  ggplot() +
  geom_boxplot(aes(y = nas_pct, x = duration_min_out)) +
  scale_fill_brewer(type = "div", palette = 'Spectral') +
  ggtitle("NA dist. by duration in minutes") +
  xlab("Duration between 0.7 min and 97.3 min = NORMAL (95% of responses)") + 
  theme_classic() +
  theme(axis.text.x = element_text(size = 9), 
        panel.background = element_rect(fill = "gray95"))

san %>% 
  filter(!is.na(zipcode)) %>% 
  ggplot() +
  geom_boxplot(aes(y = nas_pct, x = duration_min_out, fill = as.factor(zipcode))) +
  scale_fill_brewer(type = "div", palette = 'Spectral') +
  ggtitle("NA dist. by duration in minutes by zip code") +
  xlab("Duration between 0.7 min and 97.3 min = NORMAL (95% of responses)") + 
  theme_classic() +
  theme(axis.text.x = element_text(size = 9), 
        panel.background = element_rect(fill = "gray95"))

san %>% 
  filter(!is.na(zipcode), duration < 900) %>% 
  ggplot() +
  geom_boxplot(aes(y = duration, fill = as.factor(zipcode), x = nas_out)) +
  scale_fill_brewer(type = "div", palette = 'Spectral') +
  ggtitle("Duration (min) by zip code and NA distribution") +
  xlab("Zip code") + 
  theme_classic() +
  theme(axis.text.x = element_text(size = 9), 
        panel.background = element_rect(fill = "gray95"))

  1. Locate “odd responses” based on duration and number of NA in each response
  2. Duration: a) Keep responses in (2.5%, 97.5%), b) responses in (5%, 95%)
  3. NAs: a) Responses in (10%, 90%)

Part I.

view(dfSummary(san[, 5:173], plain.ascii = F, graph.magnif = .75, labels.col = T, max.string.width = 15), method = "render")

Data Frame Summary

san

Dimensions: 1013 x 169
Duplicates: 0
No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
1 Progress [numeric] Mean (sd) : 91.1 (26.8) min < med < max: 0 < 100 < 100 IQR (CV) : 0 (0.3) 35 distinct values 1013 (100.0%) 0 (0.0%)
2 Duration (in seconds) [numeric] Mean (sd) : 1746.6 (3597.5) min < med < max: 10 < 1219 < 82589 IQR (CV) : 1220 (2.1) 841 distinct values 1013 (100.0%) 0 (0.0%)
3 Finished [numeric] Min : 0 Mean : 0.9 Max : 1
0:110(10.9%)
1:903(89.1%)
 1013 (100.0%) 0 (0.0%)
4 RecordedDate [POSIXct, POSIXt] min : 2021-02-22 14:32:03 med : 2021-02-23 12:42:57 max : 2021-03-04 02:05:00 range : 9d 11H 32M 57S 984 distinct values 1013 (100.0%) 0 (0.0%)
5 ResponseId [character] 1. R_01F3YkgcZF3LT 2. R_0Ak2tVjB09xye 3. R_0Au0Xh2TGsWoJ 4. R_0BXTlGwZGOnMe 5. R_0c60ihjCKT6Rb 6. R_0CFUnzIgwaZ1t 7. R_0fxfBNmeY6qlJ 8. R_0HXvwzj23fxTW 9. R_0ilrJc7CA6LRe 10. R_0IpsG07KZKdcO [ 1003 others ]
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1(0.1%)
1003(99.0%)
 1013 (100.0%) 0 (0.0%)
6 LocationLatitude [numeric] Mean (sd) : 33.6 (5.2) min < med < max: 21.4 < 32.9 < 61.6 IQR (CV) : 7.8 (0.2) 431 distinct values 903 (89.1%) 110 (10.9%)
7 LocationLongitude [numeric] Mean (sd) : -92.6 (44.6) min < med < max: -158 < -98.5 < 117.3 IQR (CV) : 21.4 (-0.5) 432 distinct values 903 (89.1%) 110 (10.9%)
8 DistributionChannel [character] 1. anonymous
1013(100.0%)
 1013 (100.0%) 0 (0.0%)
9 UserLanguage [character] 1. EN 2. ES
1012(99.9%)
1(0.1%)
 1013 (100.0%) 0 (0.0%)
10 consent [numeric] Min : 1 Mean : 1 Max : 2
1:1012(99.9%)
2:1(0.1%)
 1013 (100.0%) 0 (0.0%)
11 disagree [numeric] 1 distinct value
1:1(100.0%)
 1 (0.1%) 1012 (99.9%)
12 zipcode [numeric] Mean (sd) : 1.5 (0.6) min < med < max: 1 < 1 < 3 IQR (CV) : 1 (0.4)
1:543(55.7%)
2:369(37.8%)
3:63(6.5%)
 975 (96.2%) 38 (3.8%)
13 per_care_1 [numeric] Mean (sd) : 7.9 (1.7) min < med < max: 0.6 < 8 < 20.5 IQR (CV) : 1.1 (0.2) 98 distinct values 896 (88.5%) 117 (11.5%)
14 per_care_2 [numeric] Mean (sd) : 1.9 (7.1) min < med < max: -99 < 1.9 < 17.9 IQR (CV) : 1.4 (3.7) 92 distinct values 896 (88.5%) 117 (11.5%)
15 per_care_3 [numeric] Mean (sd) : -0.5 (15.8) min < med < max: -99 < 1.5 < 21.4 IQR (CV) : 1 (-30.5) 87 distinct values 896 (88.5%) 117 (11.5%)
16 per_care_4 [numeric] Mean (sd) : 0 (13.5) min < med < max: -99 < 1 < 17.8 IQR (CV) : 0.9 (-477.6) 83 distinct values 896 (88.5%) 117 (11.5%)
17 per_care_wknd_1 [numeric] Min : 4 Mean : 4.6 Max : 5
4:394(44.2%)
5:498(55.8%)
 892 (88.1%) 121 (11.9%)
18 per_care_wknd_2 [numeric] Min : 4 Mean : 4.5 Max : 5
4:435(48.9%)
5:454(51.1%)
 889 (87.8%) 124 (12.2%)
19 per_care_wknd_3 [numeric] Min : 4 Mean : 4.5 Max : 5
4:472(54.2%)
5:399(45.8%)
 871 (86.0%) 142 (14.0%)
20 per_care_wknd_4 [numeric] Min : 4 Mean : 4.4 Max : 5
4:522(59.6%)
5:354(40.4%)
 876 (86.5%) 137 (13.5%)
21 pc_wknd_time_1 [numeric] Mean (sd) : 9 (5.3) min < med < max: -99 < 9 < 19 IQR (CV) : 1.9 (0.6) 86 distinct values 498 (49.2%) 515 (50.8%)
22 pc_wknd_time_2 [numeric] Mean (sd) : 3.5 (5.6) min < med < max: -99 < 2.8 < 19.1 IQR (CV) : 2.5 (1.6) 96 distinct values 453 (44.7%) 560 (55.3%)
23 pc_wknd_time_3 [numeric] Mean (sd) : 2.3 (7.6) min < med < max: -99 < 2 < 20.6 IQR (CV) : 1.5 (3.3) 86 distinct values 399 (39.4%) 614 (60.6%)
24 pc_wknd_time_4 [numeric] Mean (sd) : 2.1 (6) min < med < max: -99 < 1.5 < 19.7 IQR (CV) : 1.7 (2.9) 73 distinct values 353 (34.8%) 660 (65.2%)
25 per_care_covid_1 [numeric] Mean (sd) : 3.5 (1) min < med < max: 1 < 3 < 5 IQR (CV) : 1 (0.3)
1:32(3.6%)
2:63(7.1%)
3:364(41.0%)
4:251(28.3%)
5:177(20.0%)
 887 (87.6%) 126 (12.4%)
26 per_care_covid_2 [numeric] Mean (sd) : 3.5 (0.9) min < med < max: 1 < 3 < 5 IQR (CV) : 1 (0.2)
1:2(0.2%)
2:87(9.8%)
3:424(47.8%)
4:254(28.6%)
5:120(13.5%)
 887 (87.6%) 126 (12.4%)
27 per_care_covid_3 [numeric] Mean (sd) : 3.5 (0.9) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:10(1.1%)
2:70(7.9%)
3:445(50.2%)
4:233(26.3%)
5:124(14.0%)
6:5(0.6%)
 887 (87.6%) 126 (12.4%)
28 per_care_covid_4 [numeric] Mean (sd) : 3.3 (0.9) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:17(1.9%)
2:94(10.6%)
3:459(51.7%)
4:225(25.4%)
5:88(9.9%)
6:4(0.5%)
 887 (87.6%) 126 (12.4%)
29 st_act_1 [numeric] Mean (sd) : -19.5 (44.6) min < med < max: -99 < 3.8 < 18.4 IQR (CV) : 6.6 (-2.3) 121 distinct values 886 (87.5%) 127 (12.5%)
30 st_act_2 [numeric] Mean (sd) : -26 (45.9) min < med < max: -99 < 1.2 < 20.7 IQR (CV) : 101.5 (-1.8) 99 distinct values 886 (87.5%) 127 (12.5%)
31 st_act_3 [numeric] Mean (sd) : -30.9 (47.6) min < med < max: -99 < 0.7 < 21.4 IQR (CV) : 100.5 (-1.5) 92 distinct values 886 (87.5%) 127 (12.5%)
32 st_act_wknd_1 [numeric] Min : 3 Mean : 3.5 Max : 4
3:336(49.3%)
4:346(50.7%)
 682 (67.3%) 331 (32.7%)
33 st_act_wknd_2 [numeric] Min : 3 Mean : 3.5 Max : 4
3:297(46.3%)
4:344(53.7%)
 641 (63.3%) 372 (36.7%)
34 st_act_wknd_3 [numeric] Min : 3 Mean : 3.5 Max : 4
3:304(50.8%)
4:294(49.2%)
 598 (59.0%) 415 (41.0%)
35 sa_wknd_time_1 [numeric] Mean (sd) : -0.7 (20.3) min < med < max: -99 < 2.5 < 13.3 IQR (CV) : 2.3 (-27.4) 83 distinct values 347 (34.3%) 666 (65.7%)
36 sa_wknd_time_2 [numeric] Mean (sd) : 2.2 (12.8) min < med < max: -99 < 2.2 < 19 IQR (CV) : 3.7 (5.8) 97 distinct values 344 (34.0%) 669 (66.0%)
37 sa_wknd_time_3 [numeric] Mean (sd) : -13.8 (37.7) min < med < max: -99 < 1 < 16.4 IQR (CV) : 2.8 (-2.7) 73 distinct values 296 (29.2%) 717 (70.8%)
38 st_act_covid_1 [numeric] Mean (sd) : 3.6 (1.5) min < med < max: 1 < 3 < 6 IQR (CV) : 2 (0.4)
1:44(5.0%)
2:158(17.9%)
3:307(34.7%)
4:139(15.7%)
5:83(9.4%)
6:153(17.3%)
 884 (87.3%) 129 (12.7%)
39 st_act_covid_2 [numeric] Mean (sd) : 3.7 (1.4) min < med < max: 1 < 3 < 6 IQR (CV) : 2 (0.4)
1:30(3.4%)
2:144(16.3%)
3:287(32.5%)
4:182(20.6%)
5:79(8.9%)
6:162(18.3%)
 884 (87.3%) 129 (12.7%)
40 st_act_covid_3 [numeric] Mean (sd) : 3.5 (1.7) min < med < max: 1 < 3 < 6 IQR (CV) : 3 (0.5)
1:114(12.9%)
2:129(14.6%)
3:294(33.3%)
4:106(12.0%)
5:41(4.6%)
6:200(22.6%)
 884 (87.3%) 129 (12.7%)
41 own_device [numeric] Mean (sd) : 2.7 (0.7) min < med < max: 1 < 3 < 4 IQR (CV) : 0 (0.2)
1:97(11.0%)
2:51(5.8%)
3:719(81.3%)
4:17(1.9%)
 884 (87.3%) 129 (12.7%)
42 dev_act_1 [numeric] Mean (sd) : -2 (22.1) min < med < max: -99 < 2 < 16.8 IQR (CV) : 2.5 (-11.1) 110 distinct values 861 (85.0%) 152 (15.0%)
43 dev_act_7 [numeric] Mean (sd) : -0.2 (18.3) min < med < max: -99 < 2 < 18.6 IQR (CV) : 2.9 (-80.3) 109 distinct values 861 (85.0%) 152 (15.0%)
44 dev_act_3 [numeric] Mean (sd) : -13.2 (35.9) min < med < max: -99 < 1 < 20.8 IQR (CV) : 1.2 (-2.7) 84 distinct values 861 (85.0%) 152 (15.0%)
45 dev_act_6 [numeric] Mean (sd) : -11.1 (34.6) min < med < max: -99 < 1.3 < 20.9 IQR (CV) : 2.2 (-3.1) 102 distinct values 861 (85.0%) 152 (15.0%)
46 dev_act_wknd_1 [numeric] Min : 2 Mean : 2.4 Max : 3
2:466(56.9%)
3:353(43.1%)
 819 (80.8%) 194 (19.2%)
47 dev_act_wknd_2 [numeric] Min : 2 Mean : 2.4 Max : 3
2:484(58.1%)
3:349(41.9%)
 833 (82.2%) 180 (17.8%)
48 dev_act_wknd_3 [numeric] Min : 2 Mean : 2.5 Max : 3
2:339(46.2%)
3:395(53.8%)
 734 (72.5%) 279 (27.5%)
49 dev_act_wknd_4 [numeric] Min : 2 Mean : 2.4 Max : 3
2:460(61.5%)
3:288(38.5%)
 748 (73.8%) 265 (26.2%)
50 da_wknd_time_1 [numeric] Mean (sd) : 3.3 (5.1) min < med < max: -99 < 3 < 11.6 IQR (CV) : 1.9 (1.6) 83 distinct values 465 (45.9%) 548 (54.1%)
51 da_wknd_time_2 [numeric] Mean (sd) : 3.4 (5.4) min < med < max: -99 < 2.9 < 17.8 IQR (CV) : 2.5 (1.6) 102 distinct values 483 (47.7%) 530 (52.3%)
52 da_wknd_time_3 [numeric] Mean (sd) : 1.5 (11.3) min < med < max: -99 < 1.9 < 20.3 IQR (CV) : 2.1 (7.6) 81 distinct values 339 (33.5%) 674 (66.5%)
53 da_wknd_time_6 [numeric] Mean (sd) : 2.2 (12) min < med < max: -99 < 2.7 < 18 IQR (CV) : 2.4 (5.4) 99 distinct values 459 (45.3%) 554 (54.7%)
54 dev_act_covid_1 [numeric] Mean (sd) : 3.7 (1) min < med < max: 1 < 4 < 6 IQR (CV) : 1 (0.3)
1:25(2.9%)
2:65(7.6%)
3:287(33.5%)
4:297(34.7%)
5:168(19.6%)
6:14(1.6%)
 856 (84.5%) 157 (15.5%)
55 dev_act_covid_2 [numeric] Mean (sd) : 3.5 (1) min < med < max: 1 < 4 < 6 IQR (CV) : 1 (0.3)
1:13(1.5%)
2:118(13.8%)
3:269(31.4%)
4:311(36.3%)
5:136(15.9%)
6:9(1.1%)
 856 (84.5%) 157 (15.5%)
56 dev_act_covid_3 [numeric] Mean (sd) : 3.5 (1) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:18(2.1%)
2:93(10.9%)
3:362(42.3%)
4:249(29.1%)
5:109(12.7%)
6:25(2.9%)
 856 (84.5%) 157 (15.5%)
57 dev_act_covid_4 [numeric] Mean (sd) : 3.6 (0.9) min < med < max: 1 < 4 < 6 IQR (CV) : 1 (0.3)
1:9(1.1%)
2:69(8.1%)
3:348(40.7%)
4:294(34.3%)
5:130(15.2%)
6:6(0.7%)
 856 (84.5%) 157 (15.5%)
58 dev_act_covid_5 [numeric] Mean (sd) : 3.6 (0.9) min < med < max: 1 < 4 < 6 IQR (CV) : 1 (0.2)
1:12(1.4%)
2:56(6.5%)
3:314(36.7%)
4:323(37.7%)
5:145(16.9%)
6:6(0.7%)
 856 (84.5%) 157 (15.5%)
59 dev_act_covid_6 [numeric] Mean (sd) : 3.5 (0.9) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:9(1.1%)
2:88(10.3%)
3:352(41.1%)
4:274(32.0%)
5:124(14.5%)
6:9(1.1%)
 856 (84.5%) 157 (15.5%)
60 dev_act_covid_7 [numeric] Mean (sd) : 3.8 (1) min < med < max: 1 < 4 < 6 IQR (CV) : 1 (0.3)
1:16(1.9%)
2:52(6.1%)
3:252(29.4%)
4:333(38.9%)
5:162(18.9%)
6:41(4.8%)
 856 (84.5%) 157 (15.5%)
61 other_act_1 [numeric] Mean (sd) : -11.7 (35.7) min < med < max: -99 < 1.4 < 18.4 IQR (CV) : 2.4 (-3) 104 distinct values 871 (86.0%) 142 (14.0%)
62 other_act_2 [numeric] Mean (sd) : -2.8 (21.2) min < med < max: -99 < 1.1 < 15 IQR (CV) : 1.3 (-7.5) 81 distinct values 871 (86.0%) 142 (14.0%)
63 other_act_3 [numeric] Mean (sd) : -2.6 (20.4) min < med < max: -99 < 1.1 < 20.9 IQR (CV) : 1.1 (-8) 81 distinct values 871 (86.0%) 142 (14.0%)
64 other_act_4 [numeric] Mean (sd) : -24.1 (44.1) min < med < max: -99 < 0.7 < 16.8 IQR (CV) : 100.5 (-1.8) 84 distinct values 871 (86.0%) 142 (14.0%)
65 other_act_wknd_1 [numeric] Min : 2 Mean : 2.6 Max : 3
2:313(41.8%)
3:435(58.2%)
 748 (73.8%) 265 (26.2%)
66 other_act_wknd_2 [numeric] Min : 2 Mean : 2.4 Max : 3
2:481(57.8%)
3:351(42.2%)
 832 (82.1%) 181 (17.9%)
67 other_act_wknd_3 [numeric] Min : 2 Mean : 2.5 Max : 3
2:398(47.7%)
3:437(52.3%)
 835 (82.4%) 178 (17.6%)
68 other_act_wknd_4 [numeric] Min : 2 Mean : 2.5 Max : 3
2:316(48.5%)
3:335(51.5%)
 651 (64.3%) 362 (35.7%)
69 oa_wknd_time_1 [numeric] Mean (sd) : 2.8 (7.9) min < med < max: -99 < 2.4 < 17.8 IQR (CV) : 2.5 (2.8) 102 distinct values 575 (56.8%) 438 (43.2%)
70 oa_wknd_time_2 [numeric] Mean (sd) : 1.6 (9.4) min < med < max: -99 < 2 < 22.2 IQR (CV) : 1.4 (5.9) 83 distinct values 614 (60.6%) 399 (39.4%)
71 oa_wknd_time_3 [numeric] Mean (sd) : 1.3 (9.4) min < med < max: -99 < 1.5 < 18.8 IQR (CV) : 1.3 (7.2) 82 distinct values 617 (60.9%) 396 (39.1%)
72 oa_wknd_time_4 [numeric] Mean (sd) : 0 (14.4) min < med < max: -99 < 1.2 < 20.6 IQR (CV) : 1.5 (-1026) 79 distinct values 500 (49.4%) 513 (50.6%)
73 other_act_covid_1 [numeric] Mean (sd) : 3.5 (1) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:26(3.0%)
2:72(8.3%)
3:394(45.4%)
4:223(25.7%)
5:128(14.8%)
6:24(2.8%)
 867 (85.6%) 146 (14.4%)
74 other_act_covid_2 [numeric] Mean (sd) : 3.6 (1) min < med < max: 1 < 4 < 6 IQR (CV) : 1 (0.3)
1:12(1.4%)
2:82(9.5%)
3:321(37.0%)
4:291(33.6%)
5:152(17.5%)
6:9(1.0%)
 867 (85.6%) 146 (14.4%)
75 other_act_covid_3 [numeric] Mean (sd) : 3.5 (0.9) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:12(1.4%)
2:72(8.3%)
3:379(43.7%)
4:260(30.0%)
5:130(15.0%)
6:14(1.6%)
 867 (85.6%) 146 (14.4%)
76 other_act_covid_4 [numeric] Mean (sd) : 3.5 (1.1) min < med < max: 1 < 3 < 6 IQR (CV) : 1 (0.3)
1:27(3.1%)
2:75(8.7%)
3:395(45.6%)
4:236(27.2%)
5:76(8.8%)
6:58(6.7%)
 867 (85.6%) 146 (14.4%)
77 pre_electric [numeric] Mean (sd) : 169.6 (456.9) min < med < max: 1 < 100 < 9000 IQR (CV) : 80 (2.7) 107 distinct values 864 (85.3%) 149 (14.7%)
78 post_electric [numeric] Mean (sd) : 167 (280.3) min < med < max: 2 < 110 < 4000 IQR (CV) : 92.5 (1.7) 106 distinct values 863 (85.2%) 150 (14.8%)
79 pre_gas [numeric] Mean (sd) : 214.2 (471.8) min < med < max: 0 < 80 < 8000 IQR (CV) : 150 (2.2) 100 distinct values 861 (85.0%) 152 (15.0%)
80 post_gas [numeric] Mean (sd) : 172 (497.4) min < med < max: 0 < 80 < 9000 IQR (CV) : 70 (2.9) 98 distinct values 859 (84.8%) 154 (15.2%)
81 num_vehicles [numeric] Mean (sd) : 1.5 (0.9) min < med < max: 0 < 1 < 20 IQR (CV) : 1 (0.6)
0:26(3.0%)
1:471(54.8%)
2:329(38.3%)
3:30(3.5%)
4:1(0.1%)
5:1(0.1%)
20:1(0.1%)
 859 (84.8%) 154 (15.2%)
82 pre_trans [numeric] Mean (sd) : 374.3 (587.3) min < med < max: 0 < 210 < 9000 IQR (CV) : 280 (1.6) 94 distinct values 857 (84.6%) 156 (15.4%)
83 post_trans [numeric] Mean (sd) : 215.9 (611.6) min < med < max: 0 < 100 < 8080 IQR (CV) : 100 (2.8) 92 distinct values 857 (84.6%) 156 (15.4%)
84 health [numeric] Mean (sd) : 2.3 (0.9) min < med < max: 1 < 2 < 5 IQR (CV) : 1 (0.4)
1:141(16.5%)
2:339(39.6%)
3:324(37.9%)
4:42(4.9%)
5:10(1.2%)
 856 (84.5%) 157 (15.5%)
85 mental_health_anxious [numeric] Mean (sd) : 0 (14.6) min < med < max: -99 < 2 < 4 IQR (CV) : 2 (-1244.3)
-99:18(2.1%)
1:248(29.0%)
2:308(36.0%)
3:216(25.3%)
4:65(7.6%)
 855 (84.4%) 158 (15.6%)
86 mental_health_worry [numeric] Mean (sd) : -0.9 (16.7) min < med < max: -99 < 2 < 4 IQR (CV) : 2 (-19.6)
-99:24(2.8%)
1:328(38.4%)
2:247(28.9%)
3:201(23.5%)
4:55(6.4%)
 855 (84.4%) 158 (15.6%)
87 mental_health_interest [numeric] Mean (sd) : -0.2 (15.3) min < med < max: -99 < 2 < 4 IQR (CV) : 2 (-66.2)
-99:20(2.3%)
1:230(26.9%)
2:325(38.0%)
3:218(25.5%)
4:62(7.3%)
 855 (84.4%) 158 (15.6%)
88 mental_health_down [numeric] Mean (sd) : -1.1 (17.4) min < med < max: -99 < 2 < 4 IQR (CV) : 2 (-16.4)
-99:26(3.0%)
1:323(37.8%)
2:231(27.0%)
3:216(25.3%)
4:59(6.9%)
 855 (84.4%) 158 (15.6%)
89 physical_health [numeric] Mean (sd) : 3 (3.4) min < med < max: 0 < 2 < 28 IQR (CV) : 5 (1.1) 17 distinct values 854 (84.3%) 159 (15.7%)
90 mental_health [numeric] Mean (sd) : 3.9 (4.8) min < med < max: 0 < 2 < 30 IQR (CV) : 5 (1.2) 23 distinct values 853 (84.2%) 160 (15.8%)
91 pre_phy_health [numeric] Mean (sd) : 1.6 (7.7) min < med < max: -99 < 2 < 3 IQR (CV) : 1 (4.9)
-99:5(0.6%)
1:76(8.9%)
2:554(64.9%)
3:218(25.6%)
 853 (84.2%) 160 (15.8%)
92 pre_mental_health [numeric] Mean (sd) : 1.4 (7.7) min < med < max: -99 < 2 < 3 IQR (CV) : 0 (5.3)
-99:5(0.6%)
1:170(19.9%)
2:473(55.5%)
3:205(24.0%)
 853 (84.2%) 160 (15.8%)
93 days_poor_health [numeric] Mean (sd) : 3.1 (4.4) min < med < max: 0 < 2 < 25 IQR (CV) : 4 (1.4) 22 distinct values 853 (84.2%) 160 (15.8%)
94 impairment [numeric] Mean (sd) : -3.4 (22) min < med < max: -99 < 2 < 2 IQR (CV) : 1 (-6.4)
-99:43(5.0%)
1:285(33.4%)
2:525(61.5%)
 853 (84.2%) 160 (15.8%)
95 impairment_hsh...103 [numeric] Mean (sd) : -5 (24.9) min < med < max: -99 < 2 < 2 IQR (CV) : 1 (-5)
-99:56(6.6%)
1:273(32.0%)
2:524(61.4%)
 853 (84.2%) 160 (15.8%)
96 num_hsh_impair [numeric] Mean (sd) : 0.6 (1) min < med < max: 0 < 0 < 12 IQR (CV) : 1 (1.7)
0:517(60.6%)
1:212(24.9%)
2:100(11.7%)
3:11(1.3%)
4:7(0.8%)
5:1(0.1%)
6:3(0.4%)
10:1(0.1%)
12:1(0.1%)
 853 (84.2%) 160 (15.8%)
97 major_impairment [numeric] Mean (sd) : -7.1 (38) min < med < max: -99 < 7 < 14 IQR (CV) : 11 (-5.3) 15 distinct values 284 (28.0%) 729 (72.0%)
98 other_major_imp [numeric] 1 distinct value
0:1(100.0%)
 1 (0.1%) 1012 (99.9%)
99 impairment_hsh...107 [numeric] Mean (sd) : -8 (38.7) min < med < max: -99 < 7 < 14 IQR (CV) : 11 (-4.9) 15 distinct values 271 (26.8%) 742 (73.2%)
100 other_impairment_hsh [character] 1. A mild cold mak
1(100.0%)
 1 (0.1%) 1012 (99.9%)
101 days_impairment_1 [numeric] Mean (sd) : -30.8 (50.8) min < med < max: -99 < 2 < 31 IQR (CV) : 106 (-1.6) 26 distinct values 851 (84.0%) 162 (16.0%)
102 weeks_impairment_4 [numeric] Mean (sd) : -52.9 (50.2) min < med < max: -99 < -99 < 7 IQR (CV) : 100 (-1)
-99:461(54.2%)
0:63(7.4%)
1:153(18.0%)
2:84(9.9%)
3:48(5.6%)
4:25(2.9%)
5:9(1.1%)
6:4(0.5%)
7:4(0.5%)
 851 (84.0%) 162 (16.0%)
103 months_impairment_1 [numeric] Mean (sd) : -60.1 (49.2) min < med < max: -99 < -99 < 10 IQR (CV) : 100 (-0.8) 12 distinct values 851 (84.0%) 162 (16.0%)
104 years_impairment_1 [numeric] Mean (sd) : -63.1 (48.2) min < med < max: -99 < -99 < 7 IQR (CV) : 99 (-0.8)
-99:547(64.3%)
0:95(11.2%)
1:99(11.6%)
2:58(6.8%)
3:21(2.5%)
4:15(1.8%)
5:9(1.1%)
6:3(0.4%)
7:4(0.5%)
 851 (84.0%) 162 (16.0%)
105 med_device_1 [numeric] Mean (sd) : -51.1 (49.7) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:253(29.7%)
1:157(18.4%)
 851 (84.0%) 162 (16.0%)
106 med_device_2 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:338(39.7%)
1:72(8.5%)
 851 (84.0%) 162 (16.0%)
107 med_device_3 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:350(41.1%)
1:60(7.1%)
 851 (84.0%) 162 (16.0%)
108 med_device_4 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:364(42.8%)
1:46(5.4%)
 851 (84.0%) 162 (16.0%)
109 med_device_5 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:308(36.2%)
1:102(12.0%)
 851 (84.0%) 162 (16.0%)
110 med_device_6 [numeric] Mean (sd) : -51.3 (49.5) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:406(47.7%)
1:4(0.5%)
 851 (84.0%) 162 (16.0%)
111 med_device_7 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:313(36.8%)
1:97(11.4%)
 851 (84.0%) 162 (16.0%)
112 med_device_8 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:311(36.5%)
1:99(11.6%)
 851 (84.0%) 162 (16.0%)
113 med_device_9 [numeric] Mean (sd) : -51.2 (49.6) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:361(42.4%)
1:49(5.8%)
 851 (84.0%) 162 (16.0%)
114 med_device_10 [numeric] Mean (sd) : -51.3 (49.5) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:379(44.5%)
1:31(3.6%)
 851 (84.0%) 162 (16.0%)
115 med_device_11 [numeric] Mean (sd) : -51.3 (49.5) min < med < max: -99 < -99 < 1 IQR (CV) : 99 (-1)
-99:441(51.8%)
0:401(47.1%)
1:9(1.1%)
 851 (84.0%) 162 (16.0%)
116 other_med_device [character] 1. 0 2. glasses 3. Hh 4. no 5. No 6. Portable sleep 7. ventilator / re
2(25.0%)
1(12.5%)
1(12.5%)
1(12.5%)
1(12.5%)
1(12.5%)
1(12.5%)
 8 (0.8%) 1005 (99.2%)
117 emp_status [numeric] Mean (sd) : 1.8 (1.5) min < med < max: 1 < 1 < 7 IQR (CV) : 1.8 (0.8)
1:599(70.5%)
2:38(4.5%)
3:71(8.4%)
4:95(11.2%)
5:3(0.4%)
6:19(2.2%)
7:25(2.9%)
 850 (83.9%) 163 (16.1%)
118 emp_status_partner [numeric] Mean (sd) : 2.2 (1.8) min < med < max: 1 < 1 < 7 IQR (CV) : 2 (0.8)
1:515(60.6%)
2:52(6.1%)
3:80(9.4%)
4:125(14.7%)
5:5(0.6%)
6:15(1.8%)
7:58(6.8%)
 850 (83.9%) 163 (16.1%)
119 work_home [numeric] Mean (sd) : 0.3 (17.3) min < med < max: -99 < 3 < 4 IQR (CV) : 1 (58.1)
-99:25(2.9%)
3:568(66.9%)
4:256(30.2%)
 849 (83.8%) 164 (16.2%)
120 work_home_partner [numeric] Mean (sd) : -7.3 (28.2) min < med < max: -99 < 1 < 2 IQR (CV) : 1 (-3.9)
-99:73(8.6%)
1:489(57.6%)
2:287(33.8%)
 849 (83.8%) 164 (16.2%)
121 frontline [numeric] Mean (sd) : -0.9 (15.6) min < med < max: -99 < 2 < 2 IQR (CV) : 1 (-17.5)
-99:21(2.5%)
1:336(39.6%)
2:492(58.0%)
 849 (83.8%) 164 (16.2%)
122 frontline_partner [numeric] Mean (sd) : -6.6 (27.5) min < med < max: -99 < 2 < 2 IQR (CV) : 1 (-4.2)
-99:69(8.1%)
1:306(36.0%)
2:474(55.8%)
 849 (83.8%) 164 (16.2%)
123 hrs_work_3 [numeric] Mean (sd) : 31.8 (29.8) min < med < max: -99 < 39 < 74 IQR (CV) : 10 (0.9) 69 distinct values 848 (83.7%) 165 (16.3%)
124 hrs_work_4 [numeric] Mean (sd) : -35 (61.4) min < med < max: -99 < 0 < 80 IQR (CV) : 119 (-1.8) 65 distinct values 848 (83.7%) 165 (16.3%)
125 hrs_work_partner_3 [numeric] Mean (sd) : 18 (46.3) min < med < max: -99 < 35 < 80 IQR (CV) : 16 (2.6) 74 distinct values 848 (83.7%) 165 (16.3%)
126 hrs_work_partner_4 [numeric] Mean (sd) : -41.3 (61.9) min < med < max: -99 < -99 < 80 IQR (CV) : 116 (-1.5) 68 distinct values 848 (83.7%) 165 (16.3%)
127 min_commute_3 [numeric] Mean (sd) : 11.8 (43.5) min < med < max: -99 < 25 < 60 IQR (CV) : 20 (3.7) 59 distinct values 847 (83.6%) 166 (16.4%)
128 hrs_commute_3 [numeric] Mean (sd) : -42.7 (49.5) min < med < max: -99 < 0 < 4 IQR (CV) : 100 (-1.2)
-99:369(43.6%)
0:169(20.0%)
1:240(28.3%)
2:55(6.5%)
3:12(1.4%)
4:2(0.2%)
 847 (83.6%) 166 (16.4%)
129 min_commute_partner_3 [numeric] Mean (sd) : -0.3 (53.4) min < med < max: -99 < 21 < 60 IQR (CV) : 28.5 (-191.6) 56 distinct values 847 (83.6%) 166 (16.4%)
130 hrs_commute_partnet_3 [numeric] Mean (sd) : -49.5 (50) min < med < max: -99 < -99 < 4 IQR (CV) : 100 (-1)
-99:427(50.4%)
0:136(16.1%)
1:214(25.3%)
2:52(6.1%)
3:15(1.8%)
4:3(0.4%)
 847 (83.6%) 166 (16.4%)
131 rsn_nowork [numeric] Mean (sd) : -5.4 (27.8) min < med < max: -99 < 3 < 4 IQR (CV) : 1 (-5.1)
-99:20(8.1%)
1:8(3.2%)
2:50(20.2%)
3:144(58.3%)
4:25(10.1%)
 247 (24.4%) 766 (75.6%)
132 rsn_nowork_partner [numeric] Mean (sd) : -15.8 (39.4) min < med < max: -99 < 3 < 4 IQR (CV) : 1 (-2.5)
-99:60(18.3%)
1:5(1.5%)
2:76(23.2%)
3:154(47.0%)
4:33(10.1%)
 328 (32.4%) 685 (67.6%)
133 eip_1 [numeric] Mean (sd) : -4.3 (21.4) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-5)
-99:41(4.8%)
0:389(46.0%)
1:416(49.2%)
 846 (83.5%) 167 (16.5%)
134 eip_2 [numeric] Mean (sd) : -4.3 (21.4) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-5)
-99:41(4.8%)
0:388(45.9%)
1:417(49.3%)
 846 (83.5%) 167 (16.5%)
135 eip_3 [numeric] Mean (sd) : -4.5 (21.3) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-4.7)
-99:41(4.8%)
0:591(69.9%)
1:214(25.3%)
 846 (83.5%) 167 (16.5%)
136 eip_4 [numeric] Mean (sd) : -4.4 (21.4) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-4.9)
-99:41(4.8%)
0:451(53.3%)
1:354(41.8%)
 846 (83.5%) 167 (16.5%)
137 eip_5 [numeric] Mean (sd) : -4.4 (21.4) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-4.8)
-99:41(4.8%)
0:478(56.5%)
1:327(38.7%)
 846 (83.5%) 167 (16.5%)
138 eip_6 [numeric] Mean (sd) : -4.8 (21.3) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-4.4)
-99:41(4.8%)
0:794(93.9%)
1:11(1.3%)
 846 (83.5%) 167 (16.5%)
139 eip_7 [numeric] Mean (sd) : -4.8 (21.3) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-4.4)
-99:41(4.8%)
0:792(93.6%)
1:13(1.5%)
 846 (83.5%) 167 (16.5%)
140 other_eip [character] 1. 会 2. 0 3. 1 4. 1989 5. 200 6. 5000 7. Courses in hear 8. hui 9. no 10. shopping
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
1(9.1%)
2(18.2%)
1(9.1%)
 11 (1.1%) 1002 (98.9%)
141 year [numeric] Mean (sd) : 17.5 (7) min < med < max: 1 < 17 < 63 IQR (CV) : 8 (0.4) 42 distinct values 846 (83.5%) 167 (16.5%)
142 gender [numeric] Mean (sd) : 1.7 (0.6) min < med < max: 1 < 2 < 6 IQR (CV) : 1 (0.3)
1:286(33.8%)
2:550(65.1%)
3:2(0.2%)
4:2(0.2%)
6:5(0.6%)
 845 (83.4%) 168 (16.6%)
143 gender_7_TEXT [numeric] 1 distinct value
-99:845(100.0%)
 845 (83.4%) 168 (16.6%)
144 hispanic [numeric] Mean (sd) : 1.5 (0.5) min < med < max: 1 < 1 < 3 IQR (CV) : 1 (0.4)
1:425(50.4%)
2:404(47.9%)
3:15(1.8%)
 844 (83.3%) 169 (16.7%)
145 race [numeric] Mean (sd) : 1.4 (1) min < med < max: 1 < 1 < 8 IQR (CV) : 0 (0.7)
1:681(80.7%)
2:91(10.8%)
3:14(1.7%)
4:42(5.0%)
5:6(0.7%)
6:5(0.6%)
7:1(0.1%)
8:4(0.5%)
 844 (83.3%) 169 (16.7%)
146 education [numeric] Mean (sd) : 4.1 (6.3) min < med < max: -99 < 4 < 7 IQR (CV) : 3 (1.5)
-99:3(0.4%)
1:10(1.2%)
2:43(5.1%)
3:183(21.7%)
4:216(25.6%)
5:144(17.1%)
6:199(23.6%)
7:46(5.5%)
 844 (83.3%) 169 (16.7%)
147 marital [numeric] Mean (sd) : 2 (0.6) min < med < max: 1 < 2 < 6 IQR (CV) : 0 (0.3)
1:78(9.2%)
2:695(82.3%)
3:55(6.5%)
4:4(0.5%)
5:4(0.5%)
6:8(0.9%)
 844 (83.3%) 169 (16.7%)
148 depen [numeric] Min : 1 Mean : 1.2 Max : 2
1:650(77.0%)
2:194(23.0%)
 844 (83.3%) 169 (16.7%)
149 num_hsh [numeric] Mean (sd) : 3.7 (1.6) min < med < max: 0 < 4 < 10 IQR (CV) : 2 (0.4) 11 distinct values 844 (83.3%) 169 (16.7%)
150 num_children [numeric] Mean (sd) : 1.2 (1.7) min < med < max: 0 < 1 < 18 IQR (CV) : 0 (1.4) 11 distinct values 844 (83.3%) 169 (16.7%)
151 num_retired [numeric] Mean (sd) : 0.7 (0.9) min < med < max: 0 < 0 < 6 IQR (CV) : 1 (1.2)
0:447(53.0%)
1:196(23.2%)
2:189(22.4%)
3:4(0.5%)
4:5(0.6%)
5:2(0.2%)
6:1(0.1%)
 844 (83.3%) 169 (16.7%)
152 num_rooms [numeric] Mean (sd) : 4.4 (1.8) min < med < max: 0 < 4 < 22 IQR (CV) : 2 (0.4) 12 distinct values 844 (83.3%) 169 (16.7%)
153 num_bedrooms [numeric] Mean (sd) : 2.9 (1) min < med < max: 1 < 3 < 9 IQR (CV) : 1 (0.3)
1:42(5.0%)
2:237(28.1%)
3:359(42.6%)
4:145(17.2%)
5:49(5.8%)
6:8(1.0%)
7:1(0.1%)
9:1(0.1%)
 842 (83.1%) 171 (16.9%)
154 sq_ft [numeric] Mean (sd) : 5.3 (2.8) min < med < max: 1 < 5 < 13 IQR (CV) : 4 (0.5) 13 distinct values 844 (83.3%) 169 (16.7%)
155 hardships_1 [numeric] Mean (sd) : -11.4 (32.5) min < med < max: -99 < 1 < 1 IQR (CV) : 1 (-2.9)
-99:102(12.1%)
0:250(29.6%)
1:492(58.3%)
 844 (83.3%) 169 (16.7%)
156 hardships_2 [numeric] Mean (sd) : -11.8 (32.4) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-2.7)
-99:102(12.1%)
0:587(69.5%)
1:155(18.4%)
 844 (83.3%) 169 (16.7%)
157 hardships_3 [numeric] Mean (sd) : -11.7 (32.4) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-2.8)
-99:102(12.1%)
0:539(63.9%)
1:203(24.1%)
 844 (83.3%) 169 (16.7%)
158 hardships_4 [numeric] Mean (sd) : -11.7 (32.4) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-2.8)
-99:102(12.1%)
0:532(63.0%)
1:210(24.9%)
 844 (83.3%) 169 (16.7%)
159 hardships_5 [numeric] Mean (sd) : -11.8 (32.3) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-2.7)
-99:102(12.1%)
0:610(72.3%)
1:132(15.6%)
 844 (83.3%) 169 (16.7%)
160 hardships_6 [numeric] Mean (sd) : -11.9 (32.3) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-2.7)
-99:102(12.1%)
0:669(79.3%)
1:73(8.6%)
 844 (83.3%) 169 (16.7%)
161 welfare_1 [numeric] Mean (sd) : -9.2 (29.3) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-3.2)
-99:81(9.6%)
0:470(55.7%)
1:293(34.7%)
 844 (83.3%) 169 (16.7%)
162 welfare_2 [numeric] Mean (sd) : -9.2 (29.3) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-3.2)
-99:81(9.6%)
0:509(60.3%)
1:254(30.1%)
 844 (83.3%) 169 (16.7%)
163 welfare_3 [numeric] Mean (sd) : -9.4 (29.2) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-3.1)
-99:81(9.6%)
0:644(76.3%)
1:119(14.1%)
 844 (83.3%) 169 (16.7%)
164 welfare_4 [numeric] Mean (sd) : -9.1 (29.3) min < med < max: -99 < 0 < 1 IQR (CV) : 1 (-3.2)
-99:81(9.6%)
0:410(48.6%)
1:353(41.8%)
 844 (83.3%) 169 (16.7%)
165 welfare_5 [numeric] Mean (sd) : -9.3 (29.2) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-3.1)
-99:81(9.6%)
0:589(69.8%)
1:174(20.6%)
 844 (83.3%) 169 (16.7%)
166 welfare_6 [numeric] Mean (sd) : -9.4 (29.2) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-3.1)
-99:81(9.6%)
0:673(79.7%)
1:90(10.7%)
 844 (83.3%) 169 (16.7%)
167 welfare_7 [numeric] Mean (sd) : -9.4 (29.2) min < med < max: -99 < 0 < 1 IQR (CV) : 0 (-3.1)
-99:81(9.6%)
0:675(80.0%)
1:88(10.4%)
 844 (83.3%) 169 (16.7%)
168 email [character] Emails Valid Invalid Duplicates
839(99.9%)
1(0.1%)
34(4.0%)
 840 (82.9%) 173 (17.1%)
169 email_confirm [numeric] 1 distinct value
1:840(100.0%)
 840 (82.9%) 173 (17.1%)

Generated by summarytools 0.9.8 (R version 4.0.4)
2021-03-23

Part II.

Which responses to keep?

  1. Consider odd response those outside the “normal” range of duration (1.36, 71) AND above 91 NA values
  2. Summary table: responses that Agree to participate, valid zip code (78202 or 78230), not an odd response
### Duration and number of NAs
kable(table(san$nas_out, san$duration_min_out), caption = "NAs (%) vs Duration (min)") %>% 
  kable_classic(full_width = F) %>% 
  footnote(general = "NAs in (10%, 90%); duration in (2.5%, 97.5%)")
NAs (%) vs Duration (min)
Spend too much time Normal Spend too little time
Above 85% quantile 8 111 33
Normal 15 672 0
Below 10% quantile 3 171 0
Note:
NAs in (10%, 90%); duration in (2.5%, 97.5%) 
kable(table(san$nas_out, san$duration_min_out2), caption = "NAs (%) vs Duration (min)") %>% 
  kable_classic(full_width = F) %>% 
  footnote(general = "NAs in (10%, 90%); duration in (5%, 95%)")
NAs (%) vs Duration (min)
Above 95% quantile Normal Below 5% quantile
Above 85% quantile 10 91 51
Normal 37 650 0
Below 10% quantile 4 170 0
Note:
NAs in (10%, 90%); duration in (5%, 95%) 
kable(table(san$odd_r2), caption = "Odd responses") %>% 
  kable_classic(full_width = F)
Odd responses
Var1 Freq
Normal 952
Odd response 61 
san2 <- san %>% 
  # consent Agree and double check on survey participation. Keep zip code 78202 or 78230. Keep the first answer for unique emails
  filter(consent == "1" | disagree == "1", zipcode != 3, odd_r2 == "Normal") %>% 
  mutate(zipcode = if_else(zipcode == "1", "Elm Creek", 
                           if_else(zipcode == "2", "Jefferson Heights", NA_character_)),
         rep_email = if_else(email == lag(email, n = 1, order_by = email), "rep_email", "first_entry")) %>% 
  # Keep only the first response for those repeated emails 
  filter(rep_email == "first_entry")

#view(dfSummary(san2[, 5:173], plain.ascii = F, graph.magnif = .75, labels.col = T, max.string.width = 15), method = "render")

Section IV. Background Characteristics by Zip code

san2 %>%
  select(16, 146, 148:171, 175, 178:189) %>% 
  tbl_summary(by = zipcode, 
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                               all_categorical() ~ "{n} ({p}%)"),
              digits = list(all_continuous() ~ c(1, 1))) %>% 
  add_n() %>% 
  add_p() %>% 
  modify_header(label = "**variable**") %>% 
  modify_caption("**Section IV. Background characteristics (N = {N})**") %>% 
  bold_labels()
variable N Elm Creek, N = 4791 Jefferson Heights, N = 3261 p-value2
gender 805 >0.9
1 164 (34%) 112 (34%)
2 310 (65%) 210 (64%)
3 1 (0.2%) 1 (0.3%)
4 1 (0.2%) 1 (0.3%)
6 3 (0.6%) 2 (0.6%)
hispanic 805 0.4
1 235 (49%) 174 (53%)
2 234 (49%) 147 (45%)
3 10 (2.1%) 5 (1.5%)
race 805 0.013
1 384 (80%) 263 (81%)
2 58 (12%) 32 (9.8%)
3 9 (1.9%) 4 (1.2%)
4 20 (4.2%) 19 (5.8%)
5 0 (0%) 6 (1.8%)
6 5 (1.0%) 0 (0%)
7 0 (0%) 1 (0.3%)
8 3 (0.6%) 1 (0.3%)
education 805
-99 3 (0.6%) 0 (0%)
1 7 (1.5%) 3 (0.9%)
2 24 (5.0%) 15 (4.6%)
3 108 (23%) 67 (21%)
4 120 (25%) 88 (27%)
5 78 (16%) 59 (18%)
6 118 (25%) 73 (22%)
7 21 (4.4%) 21 (6.4%)
marital 805 0.7
1 46 (9.6%) 30 (9.2%)
2 391 (82%) 269 (83%)
3 31 (6.5%) 22 (6.7%)
4 3 (0.6%) 1 (0.3%)
5 4 (0.8%) 0 (0%)
6 4 (0.8%) 4 (1.2%)
depen 805 0.8
1 367 (77%) 247 (76%)
2 112 (23%) 79 (24%)
num_hsh 805 3.7 (1.5) 3.8 (1.6) 0.6
num_children 805 1.2 (1.5) 1.3 (2.1) 0.5
num_retired 805 0.050
0 277 (58%) 159 (49%)
1 108 (23%) 81 (25%)
2 86 (18%) 83 (25%)
3 3 (0.6%) 1 (0.3%)
4 3 (0.6%) 2 (0.6%)
5 2 (0.4%) 0 (0%)
num_rooms 805 4.2 (1.6) 4.5 (1.7) 0.028
num_bedrooms 803 0.6
1 27 (5.7%) 14 (4.3%)
2 137 (29%) 92 (28%)
3 196 (41%) 149 (46%)
4 84 (18%) 50 (15%)
5 29 (6.1%) 16 (4.9%)
6 3 (0.6%) 4 (1.2%)
7 0 (0%) 1 (0.3%)
9 1 (0.2%) 0 (0%)
Unknown 2 0
sq_ft 805 5.2 (2.8) 5.2 (2.7) >0.9
hardships_1 805 0.7
-99 56 (12%) 45 (14%)
0 146 (30%) 96 (29%)
1 277 (58%) 185 (57%)
hardships_2 805 0.6
-99 56 (12%) 45 (14%)
0 335 (70%) 219 (67%)
1 88 (18%) 62 (19%)
hardships_3 805 0.3
-99 56 (12%) 45 (14%)
0 317 (66%) 198 (61%)
1 106 (22%) 83 (25%)
hardships_4 805 0.2
-99 56 (12%) 45 (14%)
0 309 (65%) 191 (59%)
1 114 (24%) 90 (28%)
hardships_5 805 0.6
-99 56 (12%) 45 (14%)
0 349 (73%) 228 (70%)
1 74 (15%) 53 (16%)
hardships_6 805 0.12
-99 56 (12%) 45 (14%)
0 387 (81%) 245 (75%)
1 36 (7.5%) 36 (11%)
welfare_1 805 0.5
-99 52 (11%) 28 (8.6%)
0 275 (57%) 186 (57%)
1 152 (32%) 112 (34%)
welfare_2 805 0.6
-99 52 (11%) 28 (8.6%)
0 282 (59%) 197 (60%)
1 145 (30%) 101 (31%)
welfare_3 805 0.056
-99 52 (11%) 28 (8.6%)
0 373 (78%) 243 (75%)
1 54 (11%) 55 (17%)
welfare_4 805 0.15
-99 52 (11%) 28 (8.6%)
0 237 (49%) 147 (45%)
1 190 (40%) 151 (46%)
welfare_5 805 0.5
-99 52 (11%) 28 (8.6%)
0 327 (68%) 233 (71%)
1 100 (21%) 65 (20%)
welfare_6 805 0.6
-99 52 (11%) 28 (8.6%)
0 374 (78%) 263 (81%)
1 53 (11%) 35 (11%)
welfare_7 805 0.3
-99 52 (11%) 28 (8.6%)
0 371 (77%) 268 (82%)
1 56 (12%) 30 (9.2%)
number_missing_value 805 14.8 (6.6) 15.1 (6.4) 0.4
upper_out 805
97.3399999999999 479 (100%) 326 (100%)
upper_out2 805
71.24 479 (100%) 326 (100%)
lower_out 805
0.7 479 (100%) 326 (100%)
lower_out2 805
1.36 479 (100%) 326 (100%)
na_low 805
5 479 (100%) 326 (100%)
na_upp 805
58.1999999999999 479 (100%) 326 (100%)
duration_min_out 805 0.3
Spend too much time 10 (2.1%) 3 (0.9%)
Normal 469 (98%) 323 (99%)
Spend too little time 0 (0%) 0 (0%)
duration_min_out2 805 0.3
Above 95% quantile 23 (4.8%) 10 (3.1%)
Normal 456 (95%) 316 (97%)
Below 5% quantile 0 (0%) 0 (0%)
nas_out 805 0.9
Above 85% quantile 0 (0%) 0 (0%)
Normal 383 (80%) 259 (79%)
Below 10% quantile 96 (20%) 67 (21%)
odd_r 805
Normal 479 (100%) 326 (100%)
odd_r2 805
Normal 479 (100%) 326 (100%)
rep_email 805
first_entry 479 (100%) 326 (100%)

1 n (%); Mean (SD)

2 Fisher's exact test; Pearson's Chi-squared test; Wilcoxon rank sum test

Section Ia. Time Use by Zip code

san2 %>%
  select(16:80, 175, 178:189) %>% 
  tbl_summary(by = zipcode, 
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                               all_categorical() ~ "{n} ({p}%)"),
              digits = list(all_continuous() ~ c(1, 1))) %>% 
  add_n() %>% 
  add_p() %>% 
  modify_header(label = "**variable**") %>% 
  modify_caption("**Section Ia. Time Use (N = {N})**") %>% 
  bold_labels()
variable N Elm Creek, N = 4791 Jefferson Heights, N = 3261 p-value2
per_care_1 805 8.0 (1.8) 7.9 (1.6) 0.8
per_care_2 805 2.1 (6.9) 2.0 (5.9) 0.045
per_care_3 805 0.0 (14.7) -1.5 (18.3) 0.2
per_care_4 805 0.3 (12.3) -0.1 (13.7) 0.5
per_care_wknd_1 805 0.3
4 216 (45%) 136 (42%)
5 263 (55%) 190 (58%)
per_care_wknd_2 803 0.064
4 251 (53%) 149 (46%)
5 227 (47%) 176 (54%)
Unknown 1 1
per_care_wknd_3 785 0.6
4 260 (55%) 169 (53%)
5 209 (45%) 147 (47%)
Unknown 10 10
per_care_wknd_4 792 0.3
4 290 (61%) 185 (58%)
5 182 (39%) 135 (42%)
Unknown 7 6
pc_wknd_time_1 453 9.1 (2.1) 9.5 (1.9) 0.11
Unknown 216 136
pc_wknd_time_2 403 3.9 (3.1) 3.6 (2.8) 0.3
Unknown 252 150
pc_wknd_time_3 356 2.5 (7.6) 2.4 (2.0) 0.037
Unknown 270 179
pc_wknd_time_4 317 2.4 (2.7) 2.4 (2.7) >0.9
Unknown 297 191
per_care_covid_1 805 0.2
1 22 (4.6%) 8 (2.5%)
2 32 (6.7%) 26 (8.0%)
3 182 (38%) 143 (44%)
4 137 (29%) 88 (27%)
5 106 (22%) 61 (19%)
per_care_covid_2 805 0.5
1 1 (0.2%) 1 (0.3%)
2 50 (10%) 30 (9.2%)
3 226 (47%) 153 (47%)
4 133 (28%) 105 (32%)
5 69 (14%) 37 (11%)
per_care_covid_3 805 >0.9
1 6 (1.3%) 3 (0.9%)
2 37 (7.7%) 23 (7.1%)
3 242 (51%) 165 (51%)
4 124 (26%) 87 (27%)
5 68 (14%) 45 (14%)
6 2 (0.4%) 3 (0.9%)
per_care_covid_4 805 >0.9
1 10 (2.1%) 7 (2.1%)
2 53 (11%) 33 (10%)
3 238 (50%) 172 (53%)
4 128 (27%) 81 (25%)
5 48 (10%) 31 (9.5%)
6 2 (0.4%) 2 (0.6%)
st_act_1 805 -15.9 (42.3) -24.1 (47.1) 0.015
st_act_2 805 -23.8 (44.8) -29.1 (47.3) 0.016
st_act_3 805 -26.3 (45.7) -37.0 (49.4) 0.001
st_act_wknd_1 622 0.4
3 185 (48%) 123 (52%)
4 199 (52%) 115 (48%)
Unknown 95 88
st_act_wknd_2 583 0.4
3 164 (46%) 113 (50%)
4 192 (54%) 114 (50%)
Unknown 123 99
st_act_wknd_3 547 0.2
3 183 (53%) 97 (48%)
4 161 (47%) 106 (52%)
Unknown 135 123
sa_wknd_time_1 315 0.5 (17.7) -3.0 (24.7) 0.3
Unknown 279 211
sa_wknd_time_2 306 3.3 (8.3) 1.5 (13.8) 0.2
Unknown 287 212
sa_wknd_time_3 269 -9.4 (32.9) -18.8 (42.3) 0.9
Unknown 317 219
st_act_covid_1 805 0.15
1 23 (4.8%) 16 (4.9%)
2 94 (20%) 53 (16%)
3 165 (34%) 115 (35%)
4 75 (16%) 46 (14%)
5 50 (10%) 25 (7.7%)
6 72 (15%) 71 (22%)
st_act_covid_2 805 0.001
1 15 (3.1%) 11 (3.4%)
2 86 (18%) 52 (16%)
3 166 (35%) 86 (26%)
4 87 (18%) 86 (26%)
5 48 (10%) 18 (5.5%)
6 77 (16%) 73 (22%)
st_act_covid_3 805 0.007
1 58 (12%) 46 (14%)
2 84 (18%) 40 (12%)
3 167 (35%) 97 (30%)
4 62 (13%) 36 (11%)
5 22 (4.6%) 14 (4.3%)
6 86 (18%) 93 (29%)
own_device 805 0.3
1 49 (10%) 43 (13%)
2 27 (5.6%) 18 (5.5%)
3 394 (82%) 263 (81%)
4 9 (1.9%) 2 (0.6%)
dev_act_1 794 -2.2 (22.6) -1.8 (21.6) 0.6
Unknown 9 2
dev_act_7 794 0.3 (17.0) 0.2 (17.0) 0.8
Unknown 9 2
dev_act_3 794 -11.0 (33.8) -16.3 (38.7) 0.10
Unknown 9 2
dev_act_6 794 -9.7 (33.0) -13.7 (37.4) >0.9
Unknown 9 2
dev_act_wknd_1 755 0.3
2 264 (59%) 171 (55%)
3 182 (41%) 138 (45%)
Unknown 33 17
dev_act_wknd_2 772 0.7
2 267 (58%) 179 (57%)
3 190 (42%) 136 (43%)
Unknown 22 11
dev_act_wknd_3 677 0.3
2 180 (44%) 128 (48%)
3 230 (56%) 139 (52%)
Unknown 69 59
dev_act_wknd_4 687 0.3
2 249 (60%) 174 (64%)
3 165 (40%) 99 (36%)
Unknown 65 53
da_wknd_time_1 435 3.6 (1.8) 3.0 (8.1) 0.11
Unknown 215 155
da_wknd_time_2 446 3.3 (6.9) 3.6 (2.4) 0.8
Unknown 212 147
da_wknd_time_3 308 1.8 (11.1) 1.8 (9.4) 0.15
Unknown 299 198
da_wknd_time_6 423 1.4 (14.7) 3.7 (2.7) 0.042
Unknown 230 152
dev_act_covid_1 794 0.5
1 11 (2.3%) 11 (3.4%)
2 36 (7.7%) 25 (7.7%)
3 148 (31%) 115 (35%)
4 160 (34%) 112 (35%)
5 105 (22%) 57 (18%)
6 10 (2.1%) 4 (1.2%)
Unknown 9 2
dev_act_covid_2 794 0.3
1 8 (1.7%) 2 (0.6%)
2 58 (12%) 49 (15%)
3 148 (31%) 102 (31%)
4 176 (37%) 118 (36%)
5 72 (15%) 52 (16%)
6 8 (1.7%) 1 (0.3%)
Unknown 9 2
dev_act_covid_3 794 0.6
1 10 (2.1%) 7 (2.2%)
2 53 (11%) 31 (9.6%)
3 186 (40%) 147 (45%)
4 149 (32%) 87 (27%)
5 57 (12%) 42 (13%)
6 15 (3.2%) 10 (3.1%)
Unknown 9 2
dev_act_covid_4 794 >0.9
1 5 (1.1%) 2 (0.6%)
2 36 (7.7%) 26 (8.0%)
3 191 (41%) 131 (40%)
4 163 (35%) 115 (35%)
5 72 (15%) 47 (15%)
6 3 (0.6%) 3 (0.9%)
Unknown 9 2
dev_act_covid_5 794 0.5
1 8 (1.7%) 3 (0.9%)
2 31 (6.6%) 20 (6.2%)
3 165 (35%) 127 (39%)
4 190 (40%) 113 (35%)
5 72 (15%) 59 (18%)
6 4 (0.9%) 2 (0.6%)
Unknown 9 2
dev_act_covid_6 794 0.6
1 5 (1.1%) 4 (1.2%)
2 44 (9.4%) 39 (12%)
3 203 (43%) 126 (39%)
4 151 (32%) 100 (31%)
5 62 (13%) 51 (16%)
6 5 (1.1%) 4 (1.2%)
Unknown 9 2
dev_act_covid_7 794 0.027
1 6 (1.3%) 9 (2.8%)
2 31 (6.6%) 19 (5.9%)
3 145 (31%) 87 (27%)
4 196 (42%) 116 (36%)
5 70 (15%) 75 (23%)
6 22 (4.7%) 18 (5.6%)
Unknown 9 2
other_act_1 805 -12.1 (36.1) -11.7 (35.5) 0.7
other_act_2 805 -3.6 (23.0) -2.2 (19.8) 0.8
other_act_3 805 -2.6 (20.8) -2.6 (20.5) 0.5
other_act_4 805 -25.1 (44.7) -23.0 (43.4) 0.6
other_act_wknd_1 690 >0.9
2 171 (42%) 118 (42%)
3 239 (58%) 162 (58%)
Unknown 69 46
other_act_wknd_2 767 >0.9
2 261 (57%) 180 (58%)
3 193 (43%) 133 (42%)
Unknown 25 13
other_act_wknd_3 770 0.4
2 220 (48%) 140 (45%)
3 238 (52%) 172 (55%)
Unknown 21 14
other_act_wknd_4 601 0.3
2 176 (50%) 112 (45%)
3 178 (50%) 135 (55%)
Unknown 125 79
oa_wknd_time_1 532 2.9 (6.4) 2.4 (10.2) >0.9
Unknown 163 110
oa_wknd_time_2 567 1.6 (9.8) 2.0 (7.1) >0.9
Unknown 139 99
oa_wknd_time_3 570 0.9 (11.1) 2.3 (2.4) 0.030
Unknown 138 97
oa_wknd_time_4 463 -0.2 (15.0) 0.4 (12.9) 0.7
Unknown 204 138
other_act_covid_1 805 0.064
1 12 (2.5%) 13 (4.0%)
2 41 (8.6%) 29 (8.9%)
3 199 (42%) 164 (50%)
4 137 (29%) 67 (21%)
5 75 (16%) 45 (14%)
6 15 (3.1%) 8 (2.5%)
other_act_covid_2 805 0.4
1 5 (1.0%) 7 (2.1%)
2 49 (10%) 27 (8.3%)
3 170 (35%) 124 (38%)
4 158 (33%) 116 (36%)
5 90 (19%) 50 (15%)
6 7 (1.5%) 2 (0.6%)
other_act_covid_3 805 0.4
1 10 (2.1%) 2 (0.6%)
2 44 (9.2%) 21 (6.4%)
3 198 (41%) 149 (46%)
4 147 (31%) 101 (31%)
5 71 (15%) 48 (15%)
6 9 (1.9%) 5 (1.5%)
other_act_covid_4 805 0.3
1 12 (2.5%) 14 (4.3%)
2 45 (9.4%) 24 (7.4%)
3 206 (43%) 154 (47%)
4 133 (28%) 87 (27%)
5 44 (9.2%) 29 (8.9%)
6 39 (8.1%) 18 (5.5%)
number_missing_value 805 14.8 (6.6) 15.1 (6.4) 0.4
upper_out 805
97.3399999999999 479 (100%) 326 (100%)
upper_out2 805
71.24 479 (100%) 326 (100%)
lower_out 805
0.7 479 (100%) 326 (100%)
lower_out2 805
1.36 479 (100%) 326 (100%)
na_low 805
5 479 (100%) 326 (100%)
na_upp 805
58.1999999999999 479 (100%) 326 (100%)
duration_min_out 805 0.3
Spend too much time 10 (2.1%) 3 (0.9%)
Normal 469 (98%) 323 (99%)
Spend too little time 0 (0%) 0 (0%)
duration_min_out2 805 0.3
Above 95% quantile 23 (4.8%) 10 (3.1%)
Normal 456 (95%) 316 (97%)
Below 5% quantile 0 (0%) 0 (0%)
nas_out 805 0.9
Above 85% quantile 0 (0%) 0 (0%)
Normal 383 (80%) 259 (79%)
Below 10% quantile 96 (20%) 67 (21%)
odd_r 805
Normal 479 (100%) 326 (100%)
odd_r2 805
Normal 479 (100%) 326 (100%)
rep_email 805
first_entry 479 (100%) 326 (100%)

1 Mean (SD); n (%)

2 Wilcoxon rank sum test; Pearson's Chi-squared test; Fisher's exact test

Section Ib. Energy Use by Zip code

san2 %>%
  select(16, 81:87, 175, 178:189) %>% 
  tbl_summary(by = zipcode, 
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                               all_categorical() ~ "{n} ({p}%)"),
              digits = list(all_continuous() ~ c(1, 1))) %>% 
  add_n() %>% 
  add_p() %>% 
  modify_header(label = "**variable**") %>% 
  modify_caption("**Section Ib. Energy Use (N = {N})**") %>% 
  bold_labels()
variable N Elm Creek, N = 4791 Jefferson Heights, N = 3261 p-value2
pre_electric 805 172.2 (408.8) 173.8 (553.8) 0.3
post_electric 805 170.3 (304.2) 163.8 (265.3) 0.4
pre_gas 805 214.5 (382.2) 214.9 (587.1) 0.016
post_gas 805 164.3 (423.6) 185.6 (601.5) 0.6
num_vehicles 805 0.2
0 14 (2.9%) 12 (3.7%)
1 264 (55%) 167 (51%)
2 188 (39%) 130 (40%)
3 12 (2.5%) 16 (4.9%)
5 1 (0.2%) 0 (0%)
20 0 (0%) 1 (0.3%)
pre_trans 805 335.3 (483.0) 421.0 (726.2) 0.042
post_trans 805 198.2 (557.8) 255.5 (705.8) 0.051
number_missing_value 805 14.8 (6.6) 15.1 (6.4) 0.4
upper_out 805
97.3399999999999 479 (100%) 326 (100%)
upper_out2 805
71.24 479 (100%) 326 (100%)
lower_out 805
0.7 479 (100%) 326 (100%)
lower_out2 805
1.36 479 (100%) 326 (100%)
na_low 805
5 479 (100%) 326 (100%)
na_upp 805
58.1999999999999 479 (100%) 326 (100%)
duration_min_out 805 0.3
Spend too much time 10 (2.1%) 3 (0.9%)
Normal 469 (98%) 323 (99%)
Spend too little time 0 (0%) 0 (0%)
duration_min_out2 805 0.3
Above 95% quantile 23 (4.8%) 10 (3.1%)
Normal 456 (95%) 316 (97%)
Below 5% quantile 0 (0%) 0 (0%)
nas_out 805 0.9
Above 85% quantile 0 (0%) 0 (0%)
Normal 383 (80%) 259 (79%)
Below 10% quantile 96 (20%) 67 (21%)
odd_r 805
Normal 479 (100%) 326 (100%)
odd_r2 805
Normal 479 (100%) 326 (100%)
rep_email 805
first_entry 479 (100%) 326 (100%)

1 Mean (SD); n (%)

2 Wilcoxon rank sum test; Fisher's exact test

Section II. Health Conditions by Zip code

san2 %>%
  select(16, 88:101, 103, 105:119, 175, 178:189) %>% 
  tbl_summary(by = zipcode, 
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                               all_categorical() ~ "{n} ({p}%)"),
              digits = list(all_continuous() ~ c(1, 1))) %>% 
  add_n() %>% 
  add_p() %>% 
  modify_header(label = "**variable**") %>% 
  modify_caption("**Section II. Health Conditions (N = {N})**") %>% 
  bold_labels()
variable N Elm Creek, N = 4791 Jefferson Heights, N = 3261 p-value2
health 805 0.5
1 81 (17%) 49 (15%)
2 203 (42%) 123 (38%)
3 167 (35%) 131 (40%)
4 23 (4.8%) 18 (5.5%)
5 5 (1.0%) 5 (1.5%)
mental_health_anxious 805 0.002
-99 3 (0.6%) 15 (4.6%)
1 142 (30%) 92 (28%)
2 177 (37%) 115 (35%)
3 115 (24%) 85 (26%)
4 42 (8.8%) 19 (5.8%)
mental_health_worry 805 0.004
-99 6 (1.3%) 18 (5.5%)
1 185 (39%) 125 (38%)
2 142 (30%) 93 (29%)
3 109 (23%) 76 (23%)
4 37 (7.7%) 14 (4.3%)
mental_health_interest 805 0.002
-99 4 (0.8%) 16 (4.9%)
1 140 (29%) 79 (24%)
2 173 (36%) 133 (41%)
3 124 (26%) 75 (23%)
4 38 (7.9%) 23 (7.1%)
mental_health_down 805 0.028
-99 8 (1.7%) 18 (5.5%)
1 185 (39%) 123 (38%)
2 134 (28%) 81 (25%)
3 114 (24%) 84 (26%)
4 38 (7.9%) 20 (6.1%)
physical_health 805 3.2 (3.6) 2.9 (3.1) 0.7
mental_health 805 4.1 (5.0) 3.7 (4.4) 0.8
pre_phy_health 805 >0.9
-99 3 (0.6%) 2 (0.6%)
1 46 (9.6%) 28 (8.6%)
2 308 (64%) 207 (63%)
3 122 (25%) 89 (27%)
pre_mental_health 805 0.3
-99 1 (0.2%) 4 (1.2%)
1 94 (20%) 62 (19%)
2 270 (56%) 177 (54%)
3 114 (24%) 83 (25%)
days_poor_health 805 3.2 (4.5) 3.0 (4.3) 0.8
impairment 805 0.5
-99 27 (5.6%) 14 (4.3%)
1 161 (34%) 103 (32%)
2 291 (61%) 209 (64%)
impairment_hsh...103 805 0.6
-99 30 (6.3%) 24 (7.4%)
1 155 (32%) 96 (29%)
2 294 (61%) 206 (63%)
num_hsh_impair 805 0.5
0 281 (59%) 207 (63%)
1 123 (26%) 81 (25%)
2 61 (13%) 31 (9.5%)
3 7 (1.5%) 4 (1.2%)
4 5 (1.0%) 1 (0.3%)
5 1 (0.2%) 0 (0%)
6 1 (0.2%) 1 (0.3%)
10 0 (0%) 1 (0.3%)
major_impairment 264 -7.4 (38.7) -6.7 (37.1) 0.5
Unknown 318 223
impairment_hsh...107 251 -6.5 (37.0) -10.5 (41.5) 0.7
Unknown 324 230
days_impairment_1 805 -30.6 (50.8) -32.0 (51.0) 0.8
weeks_impairment_4 805
-99 263 (55%) 172 (53%)
0 40 (8.4%) 20 (6.1%)
1 74 (15%) 68 (21%)
2 48 (10%) 32 (9.8%)
3 30 (6.3%) 17 (5.2%)
4 11 (2.3%) 13 (4.0%)
5 8 (1.7%) 1 (0.3%)
6 2 (0.4%) 2 (0.6%)
7 3 (0.6%) 1 (0.3%)
months_impairment_1 805 -59.1 (49.4) -60.2 (49.3) >0.9
years_impairment_1 805
-99 303 (63%) 210 (64%)
0 61 (13%) 28 (8.6%)
1 53 (11%) 42 (13%)
2 30 (6.3%) 26 (8.0%)
3 11 (2.3%) 10 (3.1%)
4 10 (2.1%) 5 (1.5%)
5 6 (1.3%) 3 (0.9%)
6 2 (0.4%) 1 (0.3%)
7 3 (0.6%) 1 (0.3%)
med_device_1 805 0.2
-99 255 (53%) 158 (48%)
0 129 (27%) 108 (33%)
1 95 (20%) 60 (18%)
med_device_2 805 0.2
-99 255 (53%) 158 (48%)
0 188 (39%) 132 (40%)
1 36 (7.5%) 36 (11%)
med_device_3 805 0.4
-99 255 (53%) 158 (48%)
0 192 (40%) 142 (44%)
1 32 (6.7%) 26 (8.0%)
med_device_4 805 0.3
-99 255 (53%) 158 (48%)
0 196 (41%) 151 (46%)
1 28 (5.8%) 17 (5.2%)
med_device_5 805 0.4
-99 255 (53%) 158 (48%)
0 172 (36%) 126 (39%)
1 52 (11%) 42 (13%)
med_device_6 805 0.4
-99 255 (53%) 158 (48%)
0 223 (47%) 167 (51%)
1 1 (0.2%) 1 (0.3%)
med_device_7 805 0.3
-99 255 (53%) 158 (48%)
0 180 (38%) 130 (40%)
1 44 (9.2%) 38 (12%)
med_device_8 805 0.2
-99 255 (53%) 158 (48%)
0 174 (36%) 123 (38%)
1 50 (10%) 45 (14%)
med_device_9 805 0.2
-99 255 (53%) 158 (48%)
0 203 (42%) 146 (45%)
1 21 (4.4%) 22 (6.7%)
med_device_10 805 0.4
-99 255 (53%) 158 (48%)
0 208 (43%) 154 (47%)
1 16 (3.3%) 14 (4.3%)
med_device_11 805 0.4
-99 255 (53%) 158 (48%)
0 219 (46%) 165 (51%)
1 5 (1.0%) 3 (0.9%)
number_missing_value 805 14.8 (6.6) 15.1 (6.4) 0.4
upper_out 805
97.3399999999999 479 (100%) 326 (100%)
upper_out2 805
71.24 479 (100%) 326 (100%)
lower_out 805
0.7 479 (100%) 326 (100%)
lower_out2 805
1.36 479 (100%) 326 (100%)
na_low 805
5 479 (100%) 326 (100%)
na_upp 805
58.1999999999999 479 (100%) 326 (100%)
duration_min_out 805 0.3
Spend too much time 10 (2.1%) 3 (0.9%)
Normal 469 (98%) 323 (99%)
Spend too little time 0 (0%) 0 (0%)
duration_min_out2 805 0.3
Above 95% quantile 23 (4.8%) 10 (3.1%)
Normal 456 (95%) 316 (97%)
Below 5% quantile 0 (0%) 0 (0%)
nas_out 805 0.9
Above 85% quantile 0 (0%) 0 (0%)
Normal 383 (80%) 259 (79%)
Below 10% quantile 96 (20%) 67 (21%)
odd_r 805
Normal 479 (100%) 326 (100%)
odd_r2 805
Normal 479 (100%) 326 (100%)
rep_email 805
first_entry 479 (100%) 326 (100%)

1 n (%); Mean (SD)

2 Fisher's exact test; Pearson's Chi-squared test; Wilcoxon rank sum test

Section III. Employment by Zip code

san2 %>%
  select(16, 121:143, 175, 178:189) %>% 
  tbl_summary(by = zipcode, 
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                               all_categorical() ~ "{n} ({p}%)"),
              digits = list(all_continuous() ~ c(1, 1))) %>% 
  add_n() %>% 
  add_p() %>% 
  modify_header(label = "**variable**") %>% 
  modify_caption("**Section III. Employment (N = {N})**") %>% 
  bold_labels()
variable N Elm Creek, N = 4791 Jefferson Heights, N = 3261 p-value2
emp_status 805
1 336 (70%) 234 (72%)
2 25 (5.2%) 12 (3.7%)
3 48 (10%) 21 (6.4%)
4 46 (9.6%) 41 (13%)
5 1 (0.2%) 2 (0.6%)
6 9 (1.9%) 9 (2.8%)
7 14 (2.9%) 7 (2.1%)
emp_status_partner 805
1 270 (56%) 226 (69%)
2 39 (8.1%) 11 (3.4%)
3 58 (12%) 14 (4.3%)
4 68 (14%) 44 (13%)
5 3 (0.6%) 2 (0.6%)
6 9 (1.9%) 6 (1.8%)
7 32 (6.7%) 23 (7.1%)
work_home 805 0.3
-99 11 (2.3%) 12 (3.7%)
3 332 (69%) 212 (65%)
4 136 (28%) 102 (31%)
work_home_partner 805 0.9
-99 39 (8.1%) 30 (9.2%)
1 279 (58%) 188 (58%)
2 161 (34%) 108 (33%)
frontline 805 0.054
-99 11 (2.3%) 8 (2.5%)
1 176 (37%) 147 (45%)
2 292 (61%) 171 (52%)
frontline_partner 805 0.2
-99 36 (7.5%) 28 (8.6%)
1 163 (34%) 130 (40%)
2 280 (58%) 168 (52%)
hrs_work_3 805 32.3 (28.1) 30.5 (33.0) 0.6
hrs_work_4 805 -29.9 (61.5) -38.8 (61.3) 0.059
hrs_work_partner_3 805 16.8 (47.1) 21.1 (44.0) 0.4
hrs_work_partner_4 805 -36.2 (63.1) -44.7 (60.6) 0.038
min_commute_3 805 10.3 (44.1) 11.6 (43.4) 0.7
hrs_commute_3 805 0.3
-99 203 (42%) 146 (45%)
0 109 (23%) 53 (16%)
1 128 (27%) 98 (30%)
2 32 (6.7%) 22 (6.7%)
3 6 (1.3%) 6 (1.8%)
4 1 (0.2%) 1 (0.3%)
min_commute_partner_3 805 -2.6 (54.0) 3.1 (51.2) 0.2
hrs_commute_partnet_3 805 0.11
-99 251 (52%) 154 (47%)
0 84 (18%) 47 (14%)
1 102 (21%) 98 (30%)
2 32 (6.7%) 19 (5.8%)
3 8 (1.7%) 7 (2.1%)
4 2 (0.4%) 1 (0.3%)
rsn_nowork 232 0.9
-99 12 (8.5%) 8 (8.9%)
1 4 (2.8%) 4 (4.4%)
2 32 (23%) 16 (18%)
3 80 (56%) 52 (58%)
4 14 (9.9%) 10 (11%)
Unknown 337 236
rsn_nowork_partner 304 0.2
-99 32 (16%) 25 (26%)
1 3 (1.5%) 2 (2.0%)
2 49 (24%) 23 (23%)
3 101 (49%) 42 (43%)
4 21 (10%) 6 (6.1%)
Unknown 273 228
eip_1 805 0.3
-99 21 (4.4%) 17 (5.2%)
0 214 (45%) 161 (49%)
1 244 (51%) 148 (45%)
eip_2 805 0.6
-99 21 (4.4%) 17 (5.2%)
0 227 (47%) 143 (44%)
1 231 (48%) 166 (51%)
eip_3 805 0.3
-99 21 (4.4%) 17 (5.2%)
0 345 (72%) 217 (67%)
1 113 (24%) 92 (28%)
eip_4 805 0.5
-99 21 (4.4%) 17 (5.2%)
0 264 (55%) 166 (51%)
1 194 (41%) 143 (44%)
eip_5 805 0.9
-99 21 (4.4%) 17 (5.2%)
0 269 (56%) 182 (56%)
1 189 (39%) 127 (39%)
eip_6 805 0.8
-99 21 (4.4%) 17 (5.2%)
0 451 (94%) 305 (94%)
1 7 (1.5%) 4 (1.2%)
eip_7 805 0.2
-99 21 (4.4%) 17 (5.2%)
0 453 (95%) 301 (92%)
1 5 (1.0%) 8 (2.5%)
number_missing_value 805 14.8 (6.6) 15.1 (6.4) 0.4
upper_out 805
97.3399999999999 479 (100%) 326 (100%)
upper_out2 805
71.24 479 (100%) 326 (100%)
lower_out 805
0.7 479 (100%) 326 (100%)
lower_out2 805
1.36 479 (100%) 326 (100%)
na_low 805
5 479 (100%) 326 (100%)
na_upp 805
58.1999999999999 479 (100%) 326 (100%)
duration_min_out 805 0.3
Spend too much time 10 (2.1%) 3 (0.9%)
Normal 469 (98%) 323 (99%)
Spend too little time 0 (0%) 0 (0%)
duration_min_out2 805 0.3
Above 95% quantile 23 (4.8%) 10 (3.1%)
Normal 456 (95%) 316 (97%)
Below 5% quantile 0 (0%) 0 (0%)
nas_out 805 0.9
Above 85% quantile 0 (0%) 0 (0%)
Normal 383 (80%) 259 (79%)
Below 10% quantile 96 (20%) 67 (21%)
odd_r 805
Normal 479 (100%) 326 (100%)
odd_r2 805
Normal 479 (100%) 326 (100%)
rep_email 805
first_entry 479 (100%) 326 (100%)

1 n (%); Mean (SD)

2 Pearson's Chi-squared test; Wilcoxon rank sum test; Fisher's exact test

Part III.

theme_gtsummary_compact()
## Setting `Compact` theme
tbl1a <- san.ia %>% 
  select(2, 15:18, 28:30, 44:50, 63:66, 70:84) %>% 
  tbl_summary(by = zipcode, 
              statistic = list(all_continuous() ~ "{mean} ({sd})"),
              digits = list(all_continuous() ~ c(1, 1)),
              missing_text = "Not Applicable", 
              label = list(sleep.covid ~ "Sleeping time change after COVID-19 outbreak", 
                           eat.covid ~ "Eating time change after COVID-19 outbreak",
                           cook.covid ~ "Cooking time change after COVID-19 outbreak",
                           groom.covid ~ "Grooming time change after COVID-19 outbreak",
                           class.covid ~ "Attending classes time change after COVID-19 outbreak",
                           hmwrk.covid ~ "Study activities time change after COVID-19 outbreak",
                           commute.covid ~ "Commuting time change after COVID-19 outbreak",
                           tv.covid ~ "Watching TV time change after COVID-19 outbreak",
                           stream.covid ~ "Streaming time change after COVID-19 outbreak",
                           radio.covid ~ "Listening to the radio time change after COVID-19 outbreak",
                           snet.covid ~ "Social networks time change after COVID-19 outbreak",
                           text.covid ~ "Chatting on social media time change after COVID-19 outbreak",
                           pc.covid ~ "Browsing the internet time change after COVID-19 outbreak",
                           xbox.covid ~ "Playing videogames time change after COVID-19 outbreak",
                           read.covid ~ "Reading time change after COVID-19 outbreak",
                           clean.covid ~ "Cleaning time change after COVID-19 outbreak",
                           wash.covid ~ "Laundry time change after COVID-19 outbreak",
                           repair.covid ~ "Repairing time change after COVID-19 outbreak",
                           sleep.tt ~ "Hours sleeping hours",
                           eat.tt ~ "Hours eating", 
                           cook.tt ~ "Hours cooking",
                           groom.tt ~ "HoursGrooming",
                           class.tt ~ "Hours attending classes",
                           hmwrk.tt ~ "Hours studying",
                           commute.tt ~ "Hours commuting to school",
                           tv.tt ~ "Hours watching TV",
                           pc.tt ~ "Hours using a PC",
                           radio.tt ~ "Hours listening to the radio",
                           xbox.tt ~ "Hours playing videogames",
                           read.tt ~ "Hours reading",
                           clean.tt ~ "Hours cleaning",
                           wash.tt ~ "Hours doing laundry",
                           repair.tt ~ "Hours repairing")) %>% 
  add_n() %>% 
  modify_header(update = list(label ~ "**Variable**")) %>% 
  modify_spanning_header(all_stat_cols() ~ "**Section Ia. Time Use** (N = {N})") %>% 
  bold_labels() %>% 
  italicize_levels()
tbl1b <- san.ib %>% 
  select(2, pre_electric:post_trans) %>% 
  tbl_summary(by = zipcode,
              statistic = list(all_continuous() ~ "{mean} ({sd})"),
              digits = list(all_continuous() ~ c(1, 1)),
              missing_text = "Not Applicable",
              label = list(pre_electric ~ "Electric bill before COVID-19 outbreak",
                           post_electric ~ "Electric bill after COVID-19 outbreak",
                           pre_gas ~ "Gas bill before COVID-19 outbreak",
                           post_gas ~ "Gas bill after COVID-19 outbreak",
                           num_vehicles ~ "Number of vehicles",
                           pre_trans ~ "Gas for transportation before COVID-19 outbreak",
                           post_trans ~ "Gas for transportation after COVID-19 outbreak")) %>%
  add_n() %>% 
  modify_header(update = list(label ~ "**Variable**")) %>% 
  modify_spanning_header(all_stat_cols() ~ "**Section Ib. Energy Use** (N = {N})") %>% 
  bold_labels() %>% 
  italicize_levels()
tbl2 <- san.ii %>% 
  select(2:32) %>% 
  tbl_summary(by = zipcode,
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                               all_categorical() ~ "{n} ({p}%)"),
              digits = list(all_continuous() ~ c(1, 1)),
              missing_text = "Not Applicable", 
              label = list(health ~ "Health status", 
                           mental_health_anxious ~ "Feeling anxious",
                           mental_health_worry ~ "Feeling worried",
                           mental_health_interest ~ "Having little interest",
                           mental_health_down ~ "Feeling depressed",
                           physical_health ~ "Days of not good physical health",
                           mental_health ~ "Days of not good mental health",
                           pre_phy_health ~ "Physical health prior to COVID-19",
                           pre_mental_health ~ "Mental health prior to COVID-19",
                           days_poor_health ~ "Days of poor physical or mental health",
                           impairment ~ "Limited by any impairment",
                           impairment_hsh ~ "Household member limited by any impairment",
                           num_hsh_impair ~ "Household members with any impairment",
                           major_impairment ~ "Major impairment",
                           major_impairment_hsh ~ "Household member major impairment",
                           days_impairment_1 ~ "Days limited because of major impairment",
                           weeks_impairment_4 ~ "Weeks limited because of major impairment",
                           months_impairment_1 ~ "Months limited because of major impairment",
                           years_impairment_1 ~ "Years limited because of major impairment",
                           wheelchair ~ "Electric mobility scooter or wheelchair",
                           e_bed ~ "Electric bed",
                           person_lifting ~ "Person-lifting or moving equipment",
                           dialysis_machine ~ "Home dialysis machine",
                           ventilator ~ "Home ventilator (not CPAP)",
                           cpap_bipap ~ "CPAP/BIPAP machine",
                           nebulizer ~ "Ultrasonic nebulizer",
                           e_recliner ~ "Electric recliner type chair",
                           iv_pump ~ "Infusion (IV) pump ",
                           phototherapy ~ "Phototherapy machine",
                           other ~ "Other medical device")) %>%
  add_n() %>% 
  modify_header(update = list(label ~ "**Variable**")) %>% 
  modify_spanning_header(all_stat_cols() ~ "**Section II. Health Conditions** (N = {N})") %>% 
  bold_labels() %>% 
  italicize_levels()
tbl3 <- san.iii %>% 
  select(2:8, 29:32, 17:25) %>% 
  tbl_summary(by = zipcode,
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                               all_categorical() ~ "{n} ({p}%)"),
              digits = list(all_continuous() ~ c(1, 1)),
              missing_text = "Not Applicable", 
              label = list(emp_status ~ "Employment status last week",
                           emp_status_partner ~ "Partner employment status last week",
                           work_home ~ "Changed to working from home",
                           work_home_partner ~ "Partner changed to working from home",
                           frontline ~ "Frontline worker",
                           frontline_partner ~ "Partner frontline worker",
                           working_t ~ "Worked hours (weekly)",
                           working_t_p ~ "Partner worked hours (weekly)",
                           commuting_t ~ "Commuting time to work",
                           commuting_t_p ~ "Partner commuting time to work",
                           rsn_nowork ~ "Main reason for not working",
                           rsn_nowork_partner ~ "Partner main reason for not working",
                           eip_food ~ "Stimulus payment on food",
                           eip_hsh ~ "Stimulus payment on household items",
                           eip_rent ~ "Stimulus payment on rent or mortage",
                           eip_utilities ~ "Stimulus payment on utilities",
                           eip_credit ~ "Stimulus payment on paying debts",
                           eip_other ~ "Stimulus payment on other things",
                           eip_dn ~ "Don't know")) %>%
  add_n() %>% 
  modify_header(label = "**variable**") %>% 
  modify_spanning_header(all_stat_cols() ~ "**Section III. Employment** (N = {N})") %>% 
  bold_labels() %>% 
  italicize_levels()
tbl4 <- san.iv %>% 
  select(2, age:ageg, race_eth:educ, depen:welfare_7) %>% 
  tbl_summary(by = zipcode,
              statistic = list(all_continuous() ~ "{mean} ({sd})",
                               all_categorical() ~ "{n} ({p}%)"),
              digits = list(all_continuous() ~ c(1, 1)),
              missing_text = "Not Applicable", 
              label = list(age ~ "Age",
                           ageg ~ "Age group",
                           race_eth ~ "Race and Ethnicity",
                           gender2 ~ "Gender",
                           marital_st ~ "Marital status",
                           educ ~ "Education level",
                           depen ~ "Number of dependents",
                           num_hsh ~ "Number of household members",
                           num_children ~ "Number of children",
                           num_retired ~ "Number of retired",
                           num_rooms ~ "Number of rooms",
                           num_bedrooms ~ "Number of bedrooms",
                           sq_ft ~ "Square feet",
                           hardships_1 ~ "Loss of employment income",
                           hardships_2 ~ "Difficulty in paying utility bills",
                           hardships_3 ~ "Difficulty in affording the kinds of food you need",
                           hardships_4 ~ "Difficulty in paying next rent or mortgage payment",
                           hardships_5 ~ "Difficulty in paying medical bill/health insurance",
                           hardships_6 ~ "Delay getting/Do not get medical care or prescription medication",
                           welfare_1 ~ "Unemployment Insurance (UI)",
                           welfare_2 ~ "Social Security benefits",
                           welfare_3 ~ "Supplemental Security Income (SSI)",
                           welfare_4 ~ "Medicare benefits",
                           welfare_5 ~ "Stimulus (economic impact) Payment",
                           welfare_6 ~ "Supplemental Nutrition Assistance Program",
                           welfare_7 ~ "Other types of assistance from the city or federal government")) %>%
  add_n() %>% 
  modify_header(label = "**variable**") %>% 
  modify_spanning_header(all_stat_cols() ~ "**Section IV. Background Characteristics** (N = {N})") %>% 
  bold_labels() %>% 
  italicize_levels()
tbl1a
Variable N Section Ia. Time Use (N = 805)
Elm Creek, N = 4791 Jefferson Heights, N = 3261
Sleeping time change after COVID-19 outbreak 805
Much less time 22 (4.6%) 8 (2.5%)
Somewhat less time 32 (6.7%) 26 (8.0%)
About the same 182 (38%) 143 (44%)
Somewhat more time 137 (29%) 88 (27%)
Much more time 106 (22%) 61 (19%)
Eating time change after COVID-19 outbreak 805
Much less time 1 (0.2%) 1 (0.3%)
Somewhat less time 50 (10%) 30 (9.2%)
About the same 226 (47%) 153 (47%)
Somewhat more time 133 (28%) 105 (32%)
Much more time 69 (14%) 37 (11%)
Cooking time change after COVID-19 outbreak 800
Much less time 6 (1.3%) 3 (0.9%)
Somewhat less time 37 (7.8%) 23 (7.1%)
About the same 242 (51%) 165 (51%)
Somewhat more time 124 (26%) 87 (27%)
Much more time 68 (14%) 45 (14%)
Not Applicable 2 3
Grooming time change after COVID-19 outbreak 801
Much less time 10 (2.1%) 7 (2.2%)
Somewhat less time 53 (11%) 33 (10%)
About the same 238 (50%) 172 (53%)
Somewhat more time 128 (27%) 81 (25%)
Much more time 48 (10%) 31 (9.6%)
Not Applicable 2 2
Attending classes time change after COVID-19 outbreak 662
Much less time 23 (5.7%) 16 (6.3%)
Somewhat less time 94 (23%) 53 (21%)
About the same 165 (41%) 115 (45%)
Somewhat more time 75 (18%) 46 (18%)
Much more time 50 (12%) 25 (9.8%)
Not Applicable 72 71
Study activities time change after COVID-19 outbreak 655
Much less time 15 (3.7%) 11 (4.3%)
Somewhat less time 86 (21%) 52 (21%)
About the same 166 (41%) 86 (34%)
Somewhat more time 87 (22%) 86 (34%)
Much more time 48 (12%) 18 (7.1%)
Not Applicable 77 73
Commuting time change after COVID-19 outbreak 626
Much less time 58 (15%) 46 (20%)
Somewhat less time 84 (21%) 40 (17%)
About the same 167 (42%) 97 (42%)
Somewhat more time 62 (16%) 36 (15%)
Much more time 22 (5.6%) 14 (6.0%)
Not Applicable 86 93
Watching TV time change after COVID-19 outbreak 780
Much less time 11 (2.4%) 11 (3.4%)
Somewhat less time 36 (7.8%) 25 (7.8%)
About the same 148 (32%) 115 (36%)
Somewhat more time 160 (35%) 112 (35%)
Much more time 105 (23%) 57 (18%)
Not Applicable 19 6
Streaming time change after COVID-19 outbreak 785
Much less time 8 (1.7%) 2 (0.6%)
Somewhat less time 58 (13%) 49 (15%)
About the same 148 (32%) 102 (32%)
Somewhat more time 176 (38%) 118 (37%)
Much more time 72 (16%) 52 (16%)
Not Applicable 17 3
Listening to the radio time change after COVID-19 outbreak 769
Much less time 10 (2.2%) 7 (2.2%)
Somewhat less time 53 (12%) 31 (9.9%)
About the same 186 (41%) 147 (47%)
Somewhat more time 149 (33%) 87 (28%)
Much more time 57 (13%) 42 (13%)
Not Applicable 24 12
Social networks time change after COVID-19 outbreak 788
Much less time 5 (1.1%) 2 (0.6%)
Somewhat less time 36 (7.7%) 26 (8.1%)
About the same 191 (41%) 131 (41%)
Somewhat more time 163 (35%) 115 (36%)
Much more time 72 (15%) 47 (15%)
Not Applicable 12 5
Chatting on social media time change after COVID-19 outbreak 788
Much less time 8 (1.7%) 3 (0.9%)
Somewhat less time 31 (6.7%) 20 (6.2%)
About the same 165 (35%) 127 (39%)
Somewhat more time 190 (41%) 113 (35%)
Much more time 72 (15%) 59 (18%)
Not Applicable 13 4
Browsing the internet time change after COVID-19 outbreak 785
Much less time 5 (1.1%) 4 (1.2%)
Somewhat less time 44 (9.5%) 39 (12%)
About the same 203 (44%) 126 (39%)
Somewhat more time 151 (32%) 100 (31%)
Much more time 62 (13%) 51 (16%)
Not Applicable 14 6
Playing videogames time change after COVID-19 outbreak 754
Much less time 6 (1.3%) 9 (2.9%)
Somewhat less time 31 (6.9%) 19 (6.2%)
About the same 145 (32%) 87 (28%)
Somewhat more time 196 (44%) 116 (38%)
Much more time 70 (16%) 75 (25%)
Not Applicable 31 20
Reading time change after COVID-19 outbreak 782
Much less time 12 (2.6%) 13 (4.1%)
Somewhat less time 41 (8.8%) 29 (9.1%)
About the same 199 (43%) 164 (52%)
Somewhat more time 137 (30%) 67 (21%)
Much more time 75 (16%) 45 (14%)
Not Applicable 15 8
Cleaning time change after COVID-19 outbreak 796
Much less time 5 (1.1%) 7 (2.2%)
Somewhat less time 49 (10%) 27 (8.3%)
About the same 170 (36%) 124 (38%)
Somewhat more time 158 (33%) 116 (36%)
Much more time 90 (19%) 50 (15%)
Not Applicable 7 2
Laundry time change after COVID-19 outbreak 791
Much less time 10 (2.1%) 2 (0.6%)
Somewhat less time 44 (9.4%) 21 (6.5%)
About the same 198 (42%) 149 (46%)
Somewhat more time 147 (31%) 101 (31%)
Much more time 71 (15%) 48 (15%)
Not Applicable 9 5
Repairing time change after COVID-19 outbreak 748
Much less time 12 (2.7%) 14 (4.5%)
Somewhat less time 45 (10%) 24 (7.8%)
About the same 206 (47%) 154 (50%)
Somewhat more time 133 (30%) 87 (28%)
Much more time 44 (10%) 29 (9.4%)
Not Applicable 39 18
Hours sleeping hours 805 57.3 (12.1) 57.5 (11.1)
Hours eating 802 18.7 (15.4) 17.5 (13.3)
Not Applicable 2 1
Hours cooking 783 15.1 (16.2) 13.8 (13.3)
Not Applicable 11 11
HoursGrooming 792 13.0 (16.2) 12.3 (14.3)
Not Applicable 7 6
Hours attending classes 603 36.7 (20.9) 35.9 (22.4)
Not Applicable 103 99
Hours studying 575 20.5 (18.4) 19.3 (19.7)
Not Applicable 126 104
Hours commuting to school 503 16.0 (20.5) 15.8 (19.9)
Not Applicable 154 148
Hours watching TV 754 22.5 (17.2) 21.8 (16.9)
Not Applicable 33 18
Hours using a PC 771 22.1 (17.4) 22.1 (16.3)
Not Applicable 23 11
Hours listening to the radio 673 13.9 (17.5) 12.6 (14.3)
Not Applicable 71 61
Hours playing videogames 681 17.8 (18.8) 19.2 (17.4)
Not Applicable 70 54
Hours reading 687 19.8 (19.3) 18.8 (17.7)
Not Applicable 71 47
Hours cleaning 764 13.7 (15.1) 13.2 (13.2)
Not Applicable 27 14
Hours doing laundry 767 12.8 (16.3) 12.4 (13.2)
Not Applicable 24 14
Hours repairing 594 13.2 (17.4) 12.3 (15.2)
Not Applicable 131 80

1 n (%); Mean (SD) 

tbl1b
Variable N Section Ib. Energy Use (N = 805)
Elm Creek, N = 4791 Jefferson Heights, N = 3261
Electric bill before COVID-19 outbreak 805 172.2 (408.8) 173.8 (553.8)
Electric bill after COVID-19 outbreak 805 170.3 (304.2) 163.8 (265.3)
Gas bill before COVID-19 outbreak 805 214.5 (382.2) 214.9 (587.1)
Gas bill after COVID-19 outbreak 805 164.3 (423.6) 185.6 (601.5)
Number of vehicles 805
0 14 (2.9%) 12 (3.7%)
1 264 (55%) 167 (51%)
2 188 (39%) 130 (40%)
3 12 (2.5%) 16 (4.9%)
5 1 (0.2%) 0 (0%)
20 0 (0%) 1 (0.3%)
Gas for transportation before COVID-19 outbreak 805 335.3 (483.0) 421.0 (726.2)
Gas for transportation after COVID-19 outbreak 805 198.2 (557.8) 255.5 (705.8)

1 Mean (SD); n (%) 

tbl2
Variable N Section II. Health Conditions (N = 805)
Elm Creek, N = 4791 Jefferson Heights, N = 3261
Health status 805
Excellent 81 (17%) 49 (15%)
Very good 203 (42%) 123 (38%)
Good 167 (35%) 131 (40%)
Fair 23 (4.8%) 18 (5.5%)
Poor 5 (1.0%) 5 (1.5%)
Feeling anxious 787
More than half the days 115 (24%) 85 (27%)
Nearly every day 42 (8.8%) 19 (6.1%)
Not at all 142 (30%) 92 (30%)
Several days 177 (37%) 115 (37%)
Not Applicable 3 15
Feeling worried 781
More than half the days 109 (23%) 76 (25%)
Nearly every day 37 (7.8%) 14 (4.5%)
Not at all 185 (39%) 125 (41%)
Several days 142 (30%) 93 (30%)
Not Applicable 6 18
Having little interest 785
More than half the days 124 (26%) 75 (24%)
Nearly every day 38 (8.0%) 23 (7.4%)
Not at all 140 (29%) 79 (25%)
Several days 173 (36%) 133 (43%)
Not Applicable 4 16
Feeling depressed 779
More than half the days 114 (24%) 84 (27%)
Nearly every day 38 (8.1%) 20 (6.5%)
Not at all 185 (39%) 123 (40%)
Several days 134 (28%) 81 (26%)
Not Applicable 8 18
Days of not good physical health 805 3.2 (3.6) 2.9 (3.1)
Days of not good mental health 805 4.1 (5.0) 3.7 (4.4)
Physical health prior to COVID-19 800
Worse 46 (9.7%) 28 (8.6%)
About the same 308 (65%) 207 (64%)
Better 122 (26%) 89 (27%)
Not Applicable 3 2
Mental health prior to COVID-19 800
Worse 94 (20%) 62 (19%)
About the same 270 (56%) 177 (55%)
Better 114 (24%) 83 (26%)
Not Applicable 1 4
Days of poor physical or mental health 805 3.2 (4.5) 3.0 (4.3)
Limited by any impairment 764 161 (36%) 103 (33%)
Not Applicable 27 14
Household member limited by any impairment 751 155 (35%) 96 (32%)
Not Applicable 30 24
Household members with any impairment 805
0 281 (59%) 207 (63%)
1 123 (26%) 81 (25%)
2 61 (13%) 31 (9.5%)
3 7 (1.5%) 4 (1.2%)
4 5 (1.0%) 1 (0.3%)
5 1 (0.2%) 0 (0%)
6 1 (0.2%) 1 (0.3%)
10 0 (0%) 1 (0.3%)
Major impairment 226
Arthritis/rheumatism 13 (9.5%) 12 (13%)
Back or neck problem 8 (5.8%) 8 (9.0%)
Cancer 3 (2.2%) 1 (1.1%)
Depression/anxiety/emotional problem 53 (39%) 35 (39%)
Diabetes 11 (8.0%) 0 (0%)
Eye/vision problem 12 (8.8%) 6 (6.7%)
Fractures, bone/joint injury 9 (6.6%) 8 (9.0%)
Hearing problem 5 (3.6%) 1 (1.1%)
Heart problem 8 (5.8%) 3 (3.4%)
Hypertension/high blood pressure 4 (2.9%) 4 (4.5%)
Lung/breathing problem 4 (2.9%) 2 (2.2%)
Other impairment/problem 1 (0.7%) 0 (0%)
Stroke problem 2 (1.5%) 3 (3.4%)
Walking problem 4 (2.9%) 6 (6.7%)
Not Applicable 342 237
Household member major impairment 213
Arthritis/rheumatism 15 (11%) 9 (11%)
Back or neck problem 15 (11%) 8 (10%)
Cancer 0 (0%) 3 (3.8%)
Depression/anxiety/emotional problem 43 (32%) 38 (48%)
Diabetes 14 (10%) 0 (0%)
Eye/vision problem 10 (7.5%) 4 (5.1%)
Fractures, bone/joint injury 9 (6.7%) 5 (6.3%)
Hearing problem 3 (2.2%) 2 (2.5%)
Heart problem 9 (6.7%) 2 (2.5%)
Hypertension/high blood pressure 3 (2.2%) 1 (1.3%)
Lung/breathing problem 2 (1.5%) 2 (2.5%)
Other impairment/problem 1 (0.7%) 0 (0%)
Stroke problem 6 (4.5%) 2 (2.5%)
Walking problem 4 (3.0%) 3 (3.8%)
Not Applicable 345 247
Days limited because of major impairment 517 6.7 (5.5) 6.6 (4.8)
Not Applicable 169 119
Weeks limited because of major impairment 370
0 40 (19%) 20 (13%)
1 74 (34%) 68 (44%)
2 48 (22%) 32 (21%)
3 30 (14%) 17 (11%)
4 11 (5.1%) 13 (8.4%)
5 8 (3.7%) 1 (0.6%)
6 2 (0.9%) 2 (1.3%)
7 3 (1.4%) 1 (0.6%)
Not Applicable 263 172
Months limited because of major impairment 314 2.0 (2.1) 2.1 (2.0)
Not Applicable 290 201
Years limited because of major impairment 292
0 61 (35%) 28 (24%)
1 53 (30%) 42 (36%)
2 30 (17%) 26 (22%)
3 11 (6.2%) 10 (8.6%)
4 10 (5.7%) 5 (4.3%)
5 6 (3.4%) 3 (2.6%)
6 2 (1.1%) 1 (0.9%)
7 3 (1.7%) 1 (0.9%)
Not Applicable 303 210
Electric mobility scooter or wheelchair 392 95 (42%) 60 (36%)
Not Applicable 255 158
Electric bed 392 36 (16%) 36 (21%)
Not Applicable 255 158
Person-lifting or moving equipment 392 32 (14%) 26 (15%)
Not Applicable 255 158
Home dialysis machine 392 28 (12%) 17 (10%)
Not Applicable 255 158
Home ventilator (not CPAP) 392 52 (23%) 42 (25%)
Not Applicable 255 158
CPAP/BIPAP machine 392 1 (0.4%) 1 (0.6%)
Not Applicable 255 158
Ultrasonic nebulizer 392 44 (20%) 38 (23%)
Not Applicable 255 158
Electric recliner type chair 392 50 (22%) 45 (27%)
Not Applicable 255 158
Infusion (IV) pump 392 21 (9.4%) 22 (13%)
Not Applicable 255 158
Phototherapy machine 392 16 (7.1%) 14 (8.3%)
Not Applicable 255 158
Other medical device 392 5 (2.2%) 3 (1.8%)
Not Applicable 255 158

1 n (%); Mean (SD) 

tbl3
variable N Section III. Employment (N = 805)
Elm Creek, N = 4791 Jefferson Heights, N = 3261
Employment status last week 784
Actively looking for a job 46 (9.9%) 41 (13%)
Layoff from a job 25 (5.4%) 12 (3.8%)
Retired 1 (0.2%) 2 (0.6%)
Temporarily absent from job 48 (10%) 21 (6.6%)
Unable to work 9 (1.9%) 9 (2.8%)
Work for pay at a job 336 (72%) 234 (73%)
Not Applicable 14 7
Partner employment status last week 750
Actively looking for a job 68 (15%) 44 (15%)
Layoff from a job 39 (8.7%) 11 (3.6%)
Retired 3 (0.7%) 2 (0.7%)
Temporarily absent from job 58 (13%) 14 (4.6%)
Unable to work 9 (2.0%) 6 (2.0%)
Work for pay at a job 270 (60%) 226 (75%)
Not Applicable 32 23
Changed to working from home 782 332 (71%) 212 (68%)
Not Applicable 11 12
Partner changed to working from home 736 279 (63%) 188 (64%)
Not Applicable 39 30
Frontline worker 786 176 (38%) 147 (46%)
Not Applicable 11 8
Partner frontline worker 741 163 (37%) 130 (44%)
Not Applicable 36 28
Worked hours (weekly) 439 36.8 (11.5) 36.2 (11.0)
Not Applicable 204 162
Partner worked hours (weekly) 395 32.9 (16.3) 33.5 (12.2)
Not Applicable 233 177
Commuting time to work 407 1.1 (0.8) 1.3 (0.8)
Not Applicable 236 162
Partner commuting time to work 355 1.2 (0.8) 1.3 (0.8)
Not Applicable 276 174
Main reason for not working 212
Caring for someone else 32 (25%) 16 (20%)
Employer closed 80 (62%) 52 (63%)
Other reason 14 (11%) 10 (12%)
Sick with coronavirus symptoms 4 (3.1%) 4 (4.9%)
Not Applicable 349 244
Partner main reason for not working 247
Caring for someone else 49 (28%) 23 (32%)
Employer closed 101 (58%) 42 (58%)
Other reason 21 (12%) 6 (8.2%)
Sick with coronavirus symptoms 3 (1.7%) 2 (2.7%)
Not Applicable 305 253
Stimulus payment on food 767 244 (53%) 148 (48%)
Not Applicable 21 17
Stimulus payment on household items 767 231 (50%) 166 (54%)
Not Applicable 21 17
Stimulus payment on rent or mortage 767 113 (25%) 92 (30%)
Not Applicable 21 17
Stimulus payment on utilities 767 194 (42%) 143 (46%)
Not Applicable 21 17
Stimulus payment on paying debts 767 189 (41%) 127 (41%)
Not Applicable 21 17
Stimulus payment on other things 767 7 (1.5%) 4 (1.3%)
Not Applicable 21 17
Don't know 767 5 (1.1%) 8 (2.6%)
Not Applicable 21 17

1 n (%); Mean (SD) 

tbl4
variable N Section IV. Background Characteristics (N = 805)
Elm Creek, N = 4791 Jefferson Heights, N = 3261
Age 805 38.5 (6.6) 38.3 (7.5)
Age group 805
20-29 31 (6.5%) 39 (12%)
30-39 278 (58%) 172 (53%)
40-49 136 (28%) 90 (28%)
50-59 33 (6.9%) 22 (6.7%)
60+ 1 (0.2%) 3 (0.9%)
Race and Ethnicity 805
NH-White 179 (37%) 145 (44%)
NH-Black 44 (9.2%) 18 (5.5%)
NH-Asian 7 (1.5%) 3 (0.9%)
NH-AIAN 13 (2.7%) 7 (2.1%)
NH-NHPI 0 (0%) 4 (1.2%)
NH-Other 2 (0.4%) 2 (0.6%)
Hispanic 234 (49%) 147 (45%)
Gender 805
Female 164 (34%) 112 (34%)
Male 310 (65%) 210 (64%)
Other 5 (1.0%) 4 (1.2%)
Marital status 805
In a union 422 (88%) 291 (89%)
Not in a union 11 (2.3%) 5 (1.5%)
Single 46 (9.6%) 30 (9.2%)
Education level 802
College 316 (66%) 220 (67%)
Graduate 21 (4.4%) 21 (6.4%)
High School 132 (28%) 82 (25%)
Less than HS 7 (1.5%) 3 (0.9%)
Not Applicable 3 0
Number of dependents 805 367 (77%) 247 (76%)
Number of household members 805 3.7 (1.5) 3.8 (1.6)
Number of children 805 1.2 (1.5) 1.3 (2.1)
Number of retired 805
0 277 (58%) 159 (49%)
1 108 (23%) 81 (25%)
2 86 (18%) 83 (25%)
3 3 (0.6%) 1 (0.3%)
4 3 (0.6%) 2 (0.6%)
5 2 (0.4%) 0 (0%)
Number of rooms 805 4.2 (1.6) 4.5 (1.7)
Number of bedrooms 803
1 27 (5.7%) 14 (4.3%)
2 137 (29%) 92 (28%)
3 196 (41%) 149 (46%)
4 84 (18%) 50 (15%)
5 29 (6.1%) 16 (4.9%)
6 3 (0.6%) 4 (1.2%)
7 0 (0%) 1 (0.3%)
9 1 (0.2%) 0 (0%)
Not Applicable 2 0
Square feet 788
+3000 sq ft 0 (0%) 1 (0.3%)
0-499 sq ft 33 (7.0%) 22 (6.9%)
1000-1099 sq ft 55 (12%) 49 (15%)
1100-1299 sq ft 47 (10%) 35 (11%)
1300-1599 sq ft 84 (18%) 59 (18%)
1600-1899 sq ft 46 (9.8%) 26 (8.2%)
1900-2099 sq ft 26 (5.5%) 21 (6.6%)
2100-2399 sq ft 25 (5.3%) 15 (4.7%)
2400-2699 sq ft 16 (3.4%) 10 (3.1%)
2700-2999 sq ft 14 (3.0%) 6 (1.9%)
500-699 sq ft 46 (9.8%) 21 (6.6%)
700-099 sq ft 77 (16%) 54 (17%)
Not Applicable 10 7
Loss of employment income 704 277 (65%) 185 (66%)
Not Applicable 56 45
Difficulty in paying utility bills 704 88 (21%) 62 (22%)
Not Applicable 56 45
Difficulty in affording the kinds of food you need 704 106 (25%) 83 (30%)
Not Applicable 56 45
Difficulty in paying next rent or mortgage payment 704 114 (27%) 90 (32%)
Not Applicable 56 45
Difficulty in paying medical bill/health insurance 704 74 (17%) 53 (19%)
Not Applicable 56 45
Delay getting/Do not get medical care or prescription medication 704 36 (8.5%) 36 (13%)
Not Applicable 56 45
Unemployment Insurance (UI) 725 152 (36%) 112 (38%)
Not Applicable 52 28
Social Security benefits 725 145 (34%) 101 (34%)
Not Applicable 52 28
Supplemental Security Income (SSI) 725 54 (13%) 55 (18%)
Not Applicable 52 28
Medicare benefits 725 190 (44%) 151 (51%)
Not Applicable 52 28
Stimulus (economic impact) Payment 725 100 (23%) 65 (22%)
Not Applicable 52 28
Supplemental Nutrition Assistance Program 725 53 (12%) 35 (12%)
Not Applicable 52 28
Other types of assistance from the city or federal government 725 56 (13%) 30 (10%)
Not Applicable 52 28

1 Mean (SD); n (%)

Part IV. Some plots

load("~/GitHub/san/sa_survey.RData")

san %>% 
  filter(health != "NA") %>% 
  ggplot(aes(x = race_eth, fill = health)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "E", begin = .2, end = .8) +
  facet_grid(~zip) +
  ggtitle("Responses by zip code and mental health status", 
          subtitle = "How often have you been bothered by the feeling nervous, anxious, or on edge") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(anxious != "NA", worry != "NA", interest != "NA", down!= "NA") %>% 
  ggplot(aes(x = race_eth, fill = anxious)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "E", begin = .2, end = .8) +
  facet_grid(~zip) +
  ggtitle("Responses by zip code and mental health status", 
          subtitle = "How often have you been bothered by the feeling nervous, anxious, or on edge") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(anxious != "NA", worry != "NA", interest != "NA", down!= "NA", !is.na(race_eth)) %>% 
  ggplot(aes(x = race_eth, fill = worry)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "E", begin = .2, end = .8) +
  facet_grid(~ zip) +
  ggtitle("Responses by zip code and mental health status", 
          subtitle = "How often have you been bothered by not being able to stop or control worrying") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(anxious != "NA", worry != "NA", interest != "NA", down!= "NA", !is.na(race_eth)) %>% 
  ggplot(aes(x = race_eth, fill = interest)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "E", begin = .2, end = .8) +
  facet_grid(~ zip) +
  ggtitle("Responses by zip code and mental health status", 
          subtitle = "How often have you been bothered by having little interest or pleasure in doing things") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(anxious != "NA", worry != "NA", interest != "NA", down!= "NA", !is.na(race_eth)) %>% 
  ggplot(aes(x = race_eth, fill = down)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "E", begin = .2, end = .8) +
  facet_grid(~ zip) +
  ggtitle("Responses by zip code and mental health status", 
          subtitle = "How often have you been bothered by feeling down, depressed, or hopeless") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(pre_c19_ph != "NA", pre_c19_mh != "NA", !is.na(race_eth)) %>% 
  ggplot(aes(x = race_eth, fill = pre_c19_ph)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "D", begin = .2, end = .8) +
  facet_grid(~ zip) +
  ggtitle("Responses by zip code and physical health prior to COVID-19") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(pre_c19_ph != "NA", pre_c19_mh != "NA", !is.na(race_eth)) %>% 
  ggplot(aes(x = race_eth, fill = pre_c19_mh)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(.5), vjust = 0, size = 2) +
  scale_fill_viridis_d(option = "D", begin = .2, end = .8) +
  facet_grid(~ zip) +
  ggtitle("Responses by zip code and mental health prior to COVID-19") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))
san %>% 
  filter(front != "NA") %>% 
  ggplot(aes(x = race_eth, fill = front)) +
  geom_bar() +
  geom_text(aes(label = ..count..), stat = "count", position=position_stack(0.5), vjust = 0, size = 2, color = "white") +
  scale_fill_viridis_d(option = "B", begin = .2, end = .8) +
  facet_grid(~ zip) +
  ggtitle("Responses by zip code and identification as frontline worker") +
  xlab(NULL) + 
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))

san %>% 
  filter(front != "NA",  gender != "NA", work_hrs > 0 ) %>% 
  ggplot() +
  geom_col(aes(x = work_hrs, y = race_eth, color = gender, fill = gender), position = "dodge", width = .5) +
  facet_grid(~ zip) +
  scale_color_viridis_d(option = "E", begin = .2, end = .8) +
  scale_fill_viridis_d(option = "E", begin = .2, end = .8) +
  ggtitle("Responses by zip code and working hours per week") +
  xlab("Hours per week") + 
  ylab(NULL) +
  theme_light() +
  theme(axis.text.x = element_text(angle = 90, size = 9))